Illusion Game Reliability

Preprocessing

Preprocessing

library(tidyverse)
library(ggdist)
library(ggside)
library(easystats)
library(patchwork)

illusion1 <- read.csv("../data/raw_illusion1.csv") |>
  mutate(
    Illusion_Effect = fct_relevel(as.factor(Illusion_Effect), "Incongruent", "Congruent"),
    Block = as.factor(Block)
  )

illusion2 <- read.csv("../data/raw_illusion2.csv") |>
  mutate(
    Illusion_Effect = fct_relevel(as.factor(Illusion_Effect), "Incongruent", "Congruent"),
    Block = as.factor(Block)
  )

perceptual <- read.csv("../data/raw_perceptual.csv") |>
  mutate(
    Block = as.factor(Block)
  )

sub <- read.csv("../data/raw_questionnaires.csv") |> 
  mutate(
    Education = fct_relevel(Education, "Prefer not to say", "Other", "High school", "Bachelor", "Master", "Doctorate")
  )

# For prolific:
# bayestestR::point_estimate(sub$Duration_Session1)
# plot(bayestestR::estimate_density(sub$Duration_Session1))

Outlier Detection (Tasks)

# Dear participant, thank you for participating in our study. Unfortunately, our system detected multiple issues in your data (such as implausibly short responses - in particular in the 2nd part of the study, random-like pattern of answers, or the same response to different scales - as some were designed to trigger various answers), which makes it unusable. We understand that you might have been in a hurry or had some other issues, and so we kindly ask you to return your participation; we hope to open-up more slots in the future would you be interested to participate again. 

# Dear participant, thank you for participating in our study. Unfortunately, our system detected multiple issues in your data (such as implausibly short responses - in particular in the 2nd part of the study, random-like pattern of answers, or the same response to different scales - as some were designed to trigger various answers), which makes it unusable for us. We understand that you might have been in a hurry or had some other issues; we hope to open-up more slots in the future would you be interested to participate again. 

outliers_perceptual <- c(
  "S003",
  "S008"
)
outliers_perceptual_block2 <- c(
  "S226",
  "S350"
)
outliers_illusion1 <- c(
  "S008",
  "S278",
  "S389"
  )
outliers_illusion1_block2 <- c(
  "S153",
  "S226",
  "S350"
  )
outliers_illusion2 <- c(
  "S003",
  "S123",
  "S224",
  "S226",
  "S229"
)

We removed 3, 2, and 5 participants for the illusion task - session 1, perceptual task, and illusion task - session 2 respectively, upon inspection of the average error rage (when close to 50%, suggesting random answers) and/or when the reaction time distribution was implausibly fast.

Descriptive Table

data <- rbind(illusion1, illusion2, perceptual) |>
  filter(RT < 10) |>
  mutate(
    Participant = fct_rev(Participant),
    Task = fct_relevel(Task, "Illusion_Session1", "Perceptual", "Illusion_Session2")
  )

table <- data |>
  group_by(Participant, Task) |>
  summarize(
    Error = sum(Error) / n(),
    RT = mean(RT)
  ) |>
  ungroup() |>
  arrange(desc(Error)) |>
  tidyr::pivot_wider(names_from = "Task", values_from = c("Error", "RT"), names_vary = "slowest") |>
  datawizard::data_relocate(ends_with("Session2"), after = -1) |>
  datawizard::data_relocate(ends_with("Session1"), after = 1) |>
  arrange(desc(Error_Illusion_Session1))

data.frame(Participant = c("Average"), t(sapply(table[2:ncol(table)], mean, na.rm = TRUE))) |>
  rbind(table) |>
  knitr::kable() |>
  kableExtra::row_spec(1, italic = TRUE, background = "grey", color = "white") |>
  kableExtra::row_spec(which(table$Participant %in% c(outliers_perceptual, outliers_illusion1, outliers_illusion2)) + 1, background = "#EF9A9A") |>
  kableExtra::row_spec(which(table$Participant %in% c(outliers_illusion1_block2, outliers_perceptual_block2)) + 1, background = "orange") |>
  # kableExtra::column_spec(2, color="white",
  #                         background = kableExtra::spec_color(c(NA, table$Error_Illusion_Session1))) |>
  kableExtra::kable_styling(full_width = TRUE) |>
  kableExtra::scroll_box(width = "100%", height = "500px")

Participant	Error_Illusion_Session1	RT_Illusion_Session1	Error_Perceptual	RT_Perceptual	RT_Illusion_Session2	Error_Illusion_Session2
Average	0.188	0.768	0.066	0.671	0.722	0.186
S389	0.487	0.185	0.156	0.647
S008	0.417	0.610	0.500	0.347
S110	0.391	1.519	0.323	2.553	1.008	0.422
S278	0.391	0.349	0.844	0.494	0.470	0.299
S124	0.383	0.708	0.167	0.606	0.779	0.391
S319	0.372	0.787	0.042	0.656
S153	0.370	0.575	0.109	0.699	0.645	0.281
S198	0.357	0.755	0.312	0.683	0.759	0.276
S226	0.354	0.365	0.266	0.366	0.137	0.451
S002	0.346	0.713	0.115	0.872	0.945	0.396
S023	0.346	0.631	0.125	0.619
S408	0.341	0.597	0.146	0.594	0.584	0.289
S362	0.339	0.764	0.083	0.751	0.630	0.227
S232	0.336	1.223	0.219	0.802	1.119	0.360
S299	0.323	0.712	0.214	0.584	0.621	0.284
S437	0.318	1.213	0.068	0.936	0.962	0.247
S070	0.314	1.118	0.149	1.014
S422	0.312	0.511	0.203	0.480	0.505	0.359
S117	0.305	0.490	0.182	0.412	0.588	0.266
S307	0.297	0.573	0.146	0.681	0.562	0.286
S146	0.297	0.619	0.042	0.709
S151	0.292	0.570	0.177	0.542	0.501	0.229
S328	0.289	0.543	0.130	0.611	0.546	0.247
S144	0.289	0.737	0.146	0.691
S080	0.286	0.477	0.120	0.532	0.435	0.346
S479	0.284	0.459	0.047	0.523
S266	0.284	0.601	0.104	0.717	0.753	0.227
S132	0.284	0.469	0.135	0.446	0.629	0.266
S178	0.281	0.542	0.146	0.507	0.520	0.312
S350	0.280	0.733	0.351	0.314	0.649	0.281
S246	0.279	0.445	0.073	0.412	0.399	0.260
S135	0.279	0.516	0.125	0.504	0.551	0.216
S087	0.279	0.576	0.151	0.589	0.616	0.237
S359	0.276	0.669	0.078	0.598	0.612	0.237
S341	0.271	0.563	0.042	0.585	0.637	0.143
S152	0.266	0.947	0.078	0.549	0.614	0.274
S290	0.266	0.602	0.104	0.564	0.560	0.271
S224	0.263	0.683	0.214	0.494	0.409	0.354
S174	0.263	0.617	0.062	0.730	0.771	0.198
S167	0.263	0.665	0.193	0.591
S021	0.263	0.544	0.250	0.703	0.555	0.237
S384	0.260	0.707	0.120	0.647	0.662	0.174
S333	0.260	0.831	0.052	0.883	0.802	0.255
S059	0.260	0.542	0.021	0.488	0.550	0.206
S475	0.258	0.590	0.094	0.531
S348	0.258	0.658	0.109	0.575	0.897	0.224
S251	0.258	0.632	0.052	1.205	0.907	0.206
S073	0.258	0.665	0.104	0.596	0.637	0.227
S006	0.258	0.529	0.089	0.620	0.723	0.216
S195	0.255	0.637	0.068	0.564	0.677	0.211
S154	0.254	0.615	0.141	0.528	1.038	0.201
S123	0.253	0.815	0.083	0.665	0.510	0.307
S301	0.253	0.711	0.036	0.580	0.604	0.276
S334	0.253	0.932	0.052	0.838	0.817	0.143
S305	0.253	0.613	0.188	0.574	0.525	0.195
S173	0.253	0.754	0.062	0.760	0.796	0.146
S140	0.253	0.591	0.068	0.636
S136	0.253	0.549	0.078	0.528	0.677	0.201
S367	0.250	0.551	0.099	0.584	0.905	0.279
S094	0.250	0.602	0.089	0.514	0.604	0.263
S270	0.250	0.798	0.026	0.646	0.768	0.203
S166	0.250	0.520	0.120	0.472	0.485	0.240
S129	0.250	0.674	0.062	0.572	0.579	0.219
S337	0.247	0.760	0.151	0.537
S162	0.247	0.509	0.016	0.595	0.472	0.242
S133	0.247	0.493	0.099	0.583
S112	0.247	0.490	0.073	0.492	0.477	0.240
S229	0.245	0.759	0.036	0.642	0.280	0.417
S419	0.245	0.606	0.115	0.592	0.591	0.302
S306	0.245	0.557	0.141	0.419	0.468	0.268
S053	0.245	0.511	0.151	0.475	0.570	0.211
S281	0.242	0.544	0.109	0.465	0.579	0.221
S048	0.242	0.502	0.052	0.502	0.518	0.229
S330	0.240	0.603	0.078	0.462	0.639	0.282
S203	0.240	0.825	0.047	0.633	0.527	0.208
S101	0.240	0.427	0.177	0.406	0.447	0.216
S025	0.237	0.713	0.073	0.598	0.598	0.263
S156	0.237	0.665	0.089	0.524	0.715	0.260
S347	0.237	0.643	0.083	0.543	0.664	0.208
S329	0.237	1.084	0.036	0.918	1.303	0.214
S316	0.237	0.543	0.104	0.594	0.506	0.229
S007	0.234	0.894	0.089	0.770	0.688	0.253
S279	0.234	0.597	0.099	0.554	0.568	0.247
S211	0.234	0.561	0.057	0.590	0.634	0.245
S177	0.233	0.701	0.130	0.795	0.766	0.224
S217	0.232	0.558	0.089	0.482	0.476	0.242
S484	0.232	0.541	0.073	0.498	0.537	0.172
S373	0.232	0.729	0.047	0.763	0.730	0.229
S259	0.232	0.715	0.135	0.613	0.593	0.201
S247	0.232	0.497	0.021	0.473	0.538	0.185
S233	0.232	0.517	0.062	0.530	0.682	0.159
S199	0.232	0.591	0.120	0.566	0.896	0.220
S163	0.232	0.658	0.047	0.738	0.687	0.211
S036	0.232	0.845	0.068	0.749	0.709	0.208
S402	0.229	0.785	0.036	0.813	0.775	0.253
S287	0.229	0.661	0.099	0.481	0.434	0.232
S083	0.229	0.588	0.042	0.561	0.608	0.221
S063	0.228	0.885	0.042	0.797	0.912	0.156
S324	0.227	0.739	0.047	0.637	0.715	0.229
S014	0.227	0.641	0.047	0.548
S276	0.224	0.535	0.047	0.538	0.548	0.221
S248	0.224	0.483	0.083	0.478	0.671	0.183
S244	0.224	0.575	0.172	0.476	0.654	0.185
S235	0.224	0.524	0.057	0.491	0.522	0.190
S103	0.224	0.613	0.036	0.562	0.566	0.188
S097	0.224	0.791	0.068	0.528	0.609	0.193
S019	0.224	0.618	0.052	0.496	0.600	0.174
S046	0.221	0.620	0.073	0.553	0.502	0.263
S472	0.221	0.857	0.089	0.747	0.662	0.245
S497	0.221	0.819	0.052	0.789	0.873	0.240
S311	0.221	0.450	0.234	0.401	0.502	0.193
S320	0.221	0.565	0.104	0.690	0.594	0.221
S190	0.221	0.724	0.021	0.674
S042	0.221	0.560	0.161	0.493	0.544	0.221
S289	0.220	0.883	0.089	0.566	0.788	0.203
S390	0.219	0.805	0.036	0.810	0.848	0.178
S139	0.219	0.605	0.052	0.537
S109	0.219	0.736	0.135	0.641	0.722	0.198
S092	0.217	1.202	0.036	0.810	0.908	0.201
S331	0.216	0.542	0.089	0.477	0.467	0.237
S157	0.216	0.571	0.057	0.521	0.505	0.224
S295	0.216	0.949	0.021	0.582	0.656	0.219
S466	0.216	0.582	0.047	0.570	0.541	0.216
S332	0.216	0.727	0.083	0.631	0.757	0.201
S294	0.216	0.625	0.130	0.689	0.618	0.214
S079	0.216	0.586	0.052	0.588	0.637	0.188
S131	0.214	0.572	0.089	0.584	0.598	0.232
S200	0.214	0.575	0.094	0.523	0.520	0.227
S207	0.214	0.669	0.083	0.578	0.685	0.182
S102	0.214	0.514	0.120	0.526	0.504	0.195
S250	0.213	0.761	0.147	0.803	0.686	0.174
S003	0.211	0.853	0.214	0.441	0.331	0.371
S488	0.211	1.118	0.036	0.878
S458	0.211	0.626	0.047	0.548	0.578	0.185
S452	0.211	0.546	0.031	0.600	0.688	0.198
S448	0.211	0.543	0.115	0.556
S206	0.211	0.792	0.083	0.722	0.934	0.161
S188	0.211	0.865	0.047	1.043	0.830	0.177
S107	0.211	0.808	0.036	0.722	0.885	0.159
S064	0.211	0.885	0.073	0.714	0.803	0.195
S303	0.209	0.655	0.031	0.596
S453	0.208	0.714	0.125	0.570	0.848	0.233
S128	0.208	0.596	0.115	0.603	0.564	0.221
S454	0.208	0.749	0.047	0.591	0.776	0.219
S417	0.208	0.626	0.036	0.641	0.707	0.182
S371	0.208	0.593	0.057	0.496	0.662	0.193
S356	0.208	0.634	0.115	0.584	0.532	0.201
S298	0.208	0.551	0.083	0.527	0.573	0.174
S093	0.208	0.516	0.057	0.512	0.532	0.146
S043	0.208	0.688	0.057	0.823	0.615	0.180
S214	0.206	0.679	0.042	0.591
S175	0.206	1.272	0.109	1.099	1.027	0.317
S377	0.206	0.721	0.083	0.542	0.528	0.245
S481	0.206	0.559	0.026	0.562	0.522	0.219
S145	0.206	0.541	0.099	0.498	0.470	0.216
S469	0.206	0.799	0.094	0.565	0.793	0.198
S366	0.206	0.656	0.021	0.675
S336	0.206	0.691	0.104	0.560	0.630	0.193
S312	0.206	0.993	0.073	0.619	0.983	0.172
S265	0.206	0.972	0.057	0.806	1.017	0.203
S086	0.206	0.726	0.099	0.717	0.745	0.174
S283	0.204	1.663	0.177	0.682	0.967	0.187
S108	0.203	0.715	0.089	0.569	0.622	0.240
S116	0.203	0.760	0.104	0.635	0.615	0.227
S096	0.203	0.554	0.094	0.473	0.635	0.224
S304	0.203	0.749	0.120	0.534	0.507	0.221
S191	0.203	0.745	0.062	0.591	0.711	0.219
S405	0.203	0.915	0.052	0.846	0.825	0.169
S385	0.203	0.750	0.047	0.774	0.732	0.182
S113	0.203	0.758	0.073	0.643	0.710	0.188
S499	0.201	0.595	0.156	0.501	0.496	0.253
S100	0.201	0.740	0.083	0.730	0.585	0.250
S218	0.201	0.593	0.104	0.448	0.486	0.237
S272	0.201	0.578	0.073	0.501	0.520	0.232
S242	0.201	0.986	0.010	0.748	0.935	0.229
S172	0.201	0.567	0.057	0.470	0.546	0.214
S105	0.201	0.617	0.052	0.525	0.636	0.208
S490	0.201	0.812	0.052	0.824	0.715	0.193
S407	0.201	0.954	0.042	0.737	1.064	0.190
S066	0.201	0.859	0.031	0.728	0.701	0.154
S001	0.199	0.986	0.323	0.946
S483	0.198	0.657	0.047	0.675	0.887	0.156
S409	0.198	0.630	0.115	0.578	0.601	0.185
S396	0.198	0.744	0.083	0.666	0.862	0.182
S239	0.198	0.626	0.089	0.545	0.718	0.161
S477	0.195	0.553	0.083	0.571	0.486	0.221
S424	0.195	0.811	0.042	0.741	0.792	0.216
S456	0.195	0.654	0.099	0.569	0.603	0.206
S125	0.195	0.605	0.068	0.563	0.574	0.198
S375	0.195	0.635	0.083	0.573	0.757	0.164
S342	0.195	0.634	0.057	0.714
S227	0.195	0.734	0.047	0.670	0.775	0.174
S130	0.195	0.660	0.021	0.606	0.613	0.190
S052	0.195	0.537	0.031	0.541	0.666	0.172
S260	0.193	0.671	0.042	0.560	0.562	0.214
S346	0.193	0.582	0.078	0.502	0.487	0.208
S122	0.193	0.621	0.089	0.556	0.520	0.201
S495	0.193	0.761	0.042	0.713	0.806	0.156
S335	0.193	0.627	0.078	0.620	0.592	0.169
S085	0.193	0.824	0.177	0.637	1.084	0.151
S047	0.191	0.823	0.094	0.624
S418	0.190	0.857	0.016	0.827	0.768	0.245
S323	0.190	0.698	0.068	0.649	0.976	0.240
S192	0.190	0.803	0.031	0.781	0.544	0.237
S478	0.190	0.519	0.083	0.473	0.600	0.227
S072	0.190	0.925	0.057	0.960	0.811	0.219
S237	0.190	0.859	0.031	0.733	0.793	0.219
S285	0.190	1.028	0.026	0.769	0.758	0.206
S425	0.190	0.762	0.057	0.698	0.634	0.193
S223	0.190	0.563	0.057	0.517	0.571	0.164
S485	0.188	0.897	0.052	0.788	1.027	0.219
S277	0.188	0.636	0.078	0.532	0.653	0.198
S388	0.188	0.639	0.057	0.587	0.598	0.195
S411	0.188	0.966	0.005	0.685	1.348	0.189
S496	0.188	0.754	0.083	0.713	0.686	0.141
S082	0.188	0.470	0.125	0.410
S067	0.188	0.560	0.099	0.550	0.627	0.185
S300	0.185	0.673	0.021	0.677	0.488	0.268
S296	0.185	0.565	0.068	0.476	0.478	0.219
S115	0.185	0.703	0.089	0.480	0.665	0.216
S126	0.185	0.743	0.062	0.498	0.608	0.211
S111	0.185	1.007	0.057	0.798	0.738	0.206
S282	0.185	0.505	0.078	0.460	0.460	0.201
S321	0.185	0.831	0.062	0.687	0.826	0.195
S441	0.185	0.700	0.036	0.585	0.975	0.188
S494	0.185	0.584	0.021	0.552	0.567	0.164
S383	0.185	1.085	0.062	0.976	0.923	0.154
S358	0.185	0.689	0.047	0.754	0.676	0.185
S345	0.185	0.608	0.057	0.604	0.631	0.156
S267	0.185	0.530	0.042	0.418	0.429	0.169
S149	0.185	0.638	0.062	0.595	0.588	0.120
S120	0.185	0.816	0.115	0.656	0.717	0.156
S430	0.183	1.033	0.109	1.032	0.983	0.188
S444	0.182	0.926	0.333	0.836	1.184	0.161
S486	0.182	0.780	0.036	0.609	0.611	0.214
S104	0.182	0.656	0.073	0.522	0.572	0.203
S193	0.182	0.625	0.073	0.523	0.531	0.198
S252	0.182	0.775	0.109	0.618	0.816	0.193
S249	0.182	0.768	0.036	0.693	0.838	0.193
S137	0.182	0.722	0.042	0.524	0.721	0.193
S370	0.182	0.745	0.026	0.739	0.653	0.190
S487	0.182	0.625	0.068	0.559	0.733	0.143
S398	0.182	0.663	0.078	0.656	0.715	0.146
S253	0.182	0.701	0.026	0.609	0.602	0.180
S186	0.182	0.706	0.104	0.588	0.684	0.177
S150	0.182	1.545	0.031	1.134	1.429	0.164
S026	0.182	0.934	0.036	0.732	1.027	0.146
S017	0.180	0.606	0.068	0.534	0.757	0.180
S121	0.180	0.508	0.052	0.480	0.466	0.198
S225	0.180	0.803	0.057	0.587	0.685	0.193
S467	0.180	1.071	0.078	0.735	0.650	0.188
S254	0.180	0.862	0.052	0.708	0.688	0.182
S459	0.180	0.751	0.047	0.609	0.789	0.159
S449	0.180	0.829	0.099	0.682	1.024	0.159
S344	0.180	0.699	0.026	0.807	0.803	0.161
S263	0.180	0.671	0.089	0.554	0.615	0.177
S197	0.180	0.765	0.005	0.743	0.835	0.156
S040	0.180	0.929	0.026	0.829	0.963	0.164
S089	0.178	0.888	0.031	0.603	1.044	0.162
S044	0.177	0.665	0.016	0.642	0.627	0.198
S035	0.177	0.523	0.099	0.497	0.677	0.195
S030	0.177	0.718	0.068	0.546	0.587	0.193
S414	0.177	1.065	0.042	0.880
S386	0.177	0.781	0.047	0.623
S189	0.177	1.288	0.042	0.724	1.193	0.120
S013	0.177	0.706	0.062	0.563	0.636	0.159
S353	0.174	0.544	0.083	0.451	0.449	0.242
S222	0.174	0.765	0.078	0.627	0.804	0.229
S310	0.174	0.692	0.021	0.607	0.611	0.219
S286	0.174	0.708	0.047	0.652	0.630	0.203
S161	0.174	0.626	0.115	0.562	0.560	0.201
S022	0.174	0.601	0.078	0.500	0.619	0.188
S399	0.174	0.733	0.042	0.692	0.639	0.182
S034	0.174	0.792	0.099	0.645	0.656	0.180
S470	0.174	0.644	0.042	0.605	0.670	0.159
S443	0.174	0.516	0.047	0.653	0.476	0.135
S349	0.174	0.631	0.021	0.963	0.713	0.141
S269	0.174	0.642	0.047	0.559	0.652	0.146
S228	0.174	0.572	0.036	0.485	0.567	0.161
S076	0.174	0.655	0.031	0.671	0.556	0.159
S183	0.172	1.014	0.016	1.340	0.711	0.234
S338	0.172	0.859	0.010	0.731	0.591	0.229
S351	0.172	0.797	0.036	0.768	0.613	0.203
S374	0.172	1.247	0.078	0.603	0.755	0.201
S216	0.172	0.681	0.089	0.687	0.714	0.193
S434	0.172	0.895	0.031	0.861	0.961	0.190
S435	0.172	0.757	0.010	0.663	0.722	0.188
S326	0.172	0.614	0.026	0.537	0.598	0.182
S230	0.172	0.952	0.047	1.729	0.996	0.180
S410	0.172	0.762	0.068	0.586	0.625	0.174
S474	0.172	1.003	0.073	0.865	0.916	0.159
S360	0.172	0.770	0.021	0.570
S280	0.172	0.676	0.062	0.498	0.652	0.169
S262	0.172	0.587	0.109	0.529	0.510	0.172
S158	0.172	0.631	0.073	0.575	0.618	0.164
S065	0.172	0.811	0.021	0.605	0.720	0.172
S062	0.172	0.708	0.042	0.641	0.802	0.164
S209	0.169	0.564	0.079	0.509	0.572	0.190
S314	0.169	0.651	0.021	0.560	0.693	0.159
S309	0.167	0.940	0.062	0.815	0.570	0.219
S397	0.167	0.633	0.125	0.613	0.582	0.214
S033	0.167	0.752	0.083	0.698	0.716	0.198
S445	0.167	0.452	0.104	0.405	0.476	0.188
S220	0.167	0.763	0.010	0.766	0.705	0.188
S038	0.167	0.691	0.115	0.618	0.612	0.182
S245	0.167	0.697	0.057	0.579	0.602	0.174
S264	0.167	0.902	0.036	0.675	0.886	0.172
S476	0.167	0.612	0.057	0.514	0.536	0.159
S468	0.167	0.829	0.021	0.643
S450	0.167	0.733	0.047	0.530	0.610	0.154
S442	0.167	0.829	0.005	0.638	1.469	0.126
S205	0.167	1.010	0.005	0.740	0.870	0.141
S181	0.167	0.746	0.062	0.736	0.788	0.141
S143	0.167	0.972	0.036	0.770
S024	0.167	0.735	0.042	0.756	0.724	0.159
S020	0.167	0.820	0.073	0.833	0.520	0.161
S480	0.164	0.861	0.016	0.662	0.682	0.182
S465	0.164	0.876	0.057	0.896	0.792	0.172
S447	0.164	0.840	0.005	0.689	0.919	0.159
S439	0.164	0.652	0.062	0.554	0.638	0.161
S292	0.164	0.790	0.068	0.672	0.799	0.135
S284	0.164	0.922	0.021	0.731	0.794	0.133
S258	0.164	0.756	0.068	0.514
S221	0.164	0.765	0.062	0.652	0.885	0.164
S210	0.164	0.978	0.005	0.645
S050	0.164	1.025	0.016	0.714	0.919	0.164
S009	0.164	0.833	0.010	0.633	0.805	0.159
S438	0.163	1.790	0.016	1.229	0.751	0.141
S361	0.161	0.757	0.016	0.556	0.550	0.232
S196	0.161	0.788	0.016	0.545	0.679	0.190
S039	0.161	0.524	0.083	0.517	0.454	0.182
S421	0.161	0.718	0.052	0.672	0.676	0.167
S455	0.161	1.022	0.005	0.944	0.880	0.164
S429	0.161	1.266	0.031	0.905	1.026	0.151
S352	0.161	0.657	0.042	0.576	0.709	0.133
S313	0.161	0.711	0.036	0.602	0.727	0.143
S201	0.161	1.008	0.036	0.712	0.857	0.146
S054	0.161	0.576	0.057	0.535	0.665	0.141
S004	0.161	1.051	0.010	0.800	0.769	0.156
S127	0.160	1.396	0.021	1.066	1.692	0.182
S071	0.159	0.748	0.016	0.836	0.506	0.206
S413	0.159	0.909	0.057	0.689	0.736	0.195
S489	0.159	0.692	0.026	0.576	0.693	0.191
S011	0.159	0.755	0.042	0.657	0.687	0.185
S380	0.159	0.610	0.083	0.628	0.633	0.175
S308	0.159	0.605	0.016	0.536	0.605	0.174
S194	0.159	0.755	0.073	0.666	0.558	0.169
S376	0.159	0.683	0.099	0.465	0.572	0.141
S372	0.159	0.685	0.026	0.556	0.742	0.133
S363	0.159	1.236	0.099	0.918	1.440	0.156
S302	0.159	0.995	0.021	0.696	0.817	0.154
S212	0.159	0.702	0.042	0.663	0.720	0.154
S187	0.157	1.300	0.073	0.828	1.053	0.141
S068	0.156	1.011	0.052	0.624	0.786	0.185
S255	0.156	0.647	0.042	0.574	0.659	0.182
S114	0.156	0.571	0.042	0.528	0.627	0.174
S268	0.156	0.646	0.047	0.630	0.546	0.167
S395	0.156	0.612	0.036	0.530	0.674	0.109
S215	0.156	0.657	0.047	0.538	0.653	0.154
S291	0.154	1.003	0.026	0.837	0.675	0.203
S234	0.154	0.954	0.026	0.664	0.733	0.190
S032	0.154	0.550	0.031	0.585	0.500	0.167
S275	0.154	0.565	0.036	0.546	0.584	0.141
S420	0.151	1.205	0.146	1.147	0.768	0.160
S379	0.151	0.554	0.073	0.494	0.468	0.195
S315	0.151	0.728	0.047	1.003	0.700	0.195
S423	0.151	0.961	0.036	0.577	0.725	0.174
S473	0.151	0.837	0.104	0.621	0.699	0.109
S403	0.151	1.077	0.052	0.502	0.776	0.151
S119	0.149	1.968	0.021	1.564	2.123	0.152
S498	0.148	0.715	0.042	0.587	0.616	0.198
S088	0.148	1.056	0.026	0.878	0.754	0.180
S077	0.148	0.884	0.057	1.019	0.901	0.169
S436	0.148	0.660	0.036	0.550	0.553	0.167
S165	0.148	0.908	0.021	0.679	1.038	0.156
S391	0.148	0.810	0.031	0.639	0.839	0.151
S482	0.148	0.772	0.031	0.500	0.856	0.122
S261	0.148	0.714	0.031	0.674
S155	0.148	0.688	0.042	0.838	0.841	0.122
S147	0.148	0.946	0.016	0.682
S369	0.146	0.680	0.036	0.536	0.665	0.169
S057	0.146	0.645	0.021	0.562	0.695	0.159
S440	0.146	1.028	0.036	0.817	1.058	0.151
S493	0.146	0.733	0.026	0.550	0.649	0.112
S462	0.146	0.901	0.042	0.780	1.038	0.130
S256	0.146	0.716	0.021	0.594	0.734	0.120
S240	0.146	0.768	0.042	0.536	0.678	0.117
S236	0.146	0.861	0.000	0.635	0.786	0.133
S213	0.146	0.653	0.036	0.549	0.712	0.135
S074	0.146	0.730	0.052	0.533	0.667	0.143
S058	0.146	0.609	0.036	0.596
S288	0.143	0.881	0.016	0.691	0.492	0.240
S355	0.143	0.737	0.026	0.698	0.677	0.167
S491	0.143	0.593	0.068	0.525	0.645	0.146
S339	0.143	0.859	0.068	0.831
S084	0.143	0.932	0.000	0.966	0.877	0.141
S317	0.141	0.692	0.026	0.620	0.584	0.180
S404	0.141	1.462	0.010	1.049	1.623	0.172
S164	0.141	0.512	0.010	0.629	0.496	0.167
S273	0.141	0.699	0.094	0.677	0.695	0.157
S325	0.141	1.068	0.016	0.859	0.973	0.156
S471	0.141	0.844	0.057	0.799
S343	0.141	0.761	0.010	0.686	0.717	0.122
S202	0.141	0.641	0.062	0.513	0.658	0.125
S142	0.141	0.518	0.083	0.485	0.565	0.133
S045	0.141	0.730	0.010	0.912	0.762	0.128
S160	0.138	0.640	0.057	0.526	0.581	0.198
S387	0.138	0.679	0.036	0.637	0.651	0.174
S274	0.138	0.731	0.036	0.738	0.748	0.151
S392	0.138	0.738	0.042	0.727	0.694	0.130
S368	0.138	0.854	0.047	0.707	0.720	0.120
S179	0.138	0.738	0.026	0.725	0.935	0.109
S168	0.138	0.631	0.078	0.646	0.738	0.130
S138	0.138	0.888	0.031	0.629	0.879	0.107
S340	0.136	1.142	0.047	1.066	1.200	0.131
S428	0.135	0.736	0.068	0.569	0.611	0.169
S015	0.135	0.580	0.047	0.643	0.556	0.151
S381	0.135	0.794	0.021	0.679	0.737	0.125
S118	0.135	1.150	0.005	0.974
S098	0.134	0.993	0.021	0.849
S461	0.133	0.692	0.042	0.569	0.557	0.188
S464	0.133	0.690	0.073	0.582	0.572	0.167
S394	0.133	0.642	0.021	0.590	0.742	0.156
S219	0.133	0.907	0.016	0.718	0.798	0.135
S148	0.133	0.764	0.062	0.778	0.785	0.107
S095	0.133	1.531	0.021	0.931	1.470	0.117
S041	0.130	0.751	0.052	0.672	0.665	0.177
S171	0.130	1.159	0.036	0.856	0.940	0.172
S208	0.130	1.116	0.026	0.804	1.126	0.159
S180	0.130	0.742	0.031	0.616	0.679	0.151
S081	0.130	0.733	0.021	0.718	0.654	0.146
S185	0.130	0.802	0.005	0.579	0.628	0.143
S060	0.130	0.947	0.042	0.695	1.059	0.141
S075	0.130	1.089	0.068	0.890	1.032	0.135
S500	0.130	1.020	0.016	0.753	0.821	0.083
S460	0.130	0.647	0.036	0.583	0.628	0.117
S427	0.130	0.672	0.036	0.601	0.595	0.117
S243	0.130	0.705	0.000	0.676	0.654	0.130
S091	0.130	0.673	0.031	0.561
S365	0.128	0.828	0.000	0.658	0.715	0.167
S231	0.128	0.784	0.016	0.644	0.644	0.154
S037	0.128	0.965	0.047	0.756	0.823	0.151
S051	0.128	0.753	0.016	0.569	0.653	0.141
S016	0.128	0.678	0.021	0.605	0.515	0.138
S018	0.128	0.816	0.052	0.682	0.717	0.135
S446	0.128	0.838	0.010	0.838	0.735	0.120
S357	0.128	0.837	0.000	0.636	0.838	0.107
S318	0.128	1.066	0.000	0.955
S061	0.126	1.281	0.021	0.593	0.868	0.141
S463	0.125	0.841	0.026	0.659	0.858	0.199
S055	0.125	0.756	0.057	0.553	0.596	0.167
S416	0.125	1.056	0.026	1.011	0.899	0.148
S431	0.125	0.664	0.036	0.772	0.630	0.133
S141	0.125	0.657	0.062	0.572	0.640	0.125
S031	0.125	0.780	0.016	0.669	0.751	0.104
S005	0.125	0.877	0.000	0.999	0.731	0.091
S182	0.123	1.166	0.031	0.846	0.781	0.135
S010	0.122	1.152	0.068	1.439	0.968	0.159
S271	0.122	1.322	0.026	0.993	1.175	0.141
S099	0.122	0.691	0.016	0.696	0.636	0.141
S238	0.122	0.965	0.031	0.738	0.843	0.138
S415	0.122	1.296	0.021	0.780	0.954	0.130
S027	0.122	0.730	0.010	0.706	0.672	0.102
S378	0.120	1.135	0.016	0.781	0.810	0.146
S393	0.120	0.681	0.031	0.537	0.612	0.125
S426	0.120	0.930	0.005	0.711
S134	0.120	0.688	0.021	0.635	0.681	0.117
S433	0.117	1.244	0.016	0.732	1.118	0.135
S354	0.117	0.920	0.016	0.648	0.868	0.128
S204	0.117	0.719	0.026	0.623	0.644	0.117
S169	0.117	1.828	0.036	1.431
S028	0.117	0.887	0.052	0.775	0.835	0.102
S241	0.115	0.740	0.109	0.587	0.607	0.190
S176	0.115	1.080	0.016	0.606	1.001	0.146
S056	0.115	0.664	0.052	0.568	0.596	0.146
S106	0.115	0.581	0.016	0.543
S364	0.112	0.829	0.068	0.607	0.618	0.161
S432	0.112	1.023	0.005	0.869	0.934	0.141
S184	0.111	1.733	0.016	1.934	2.264	0.226
S297	0.109	0.788	0.036	0.593	0.678	0.148
S159	0.109	0.738	0.036	0.724	0.673	0.125
S457	0.109	0.834	0.047	0.694	0.793	0.109
S382	0.109	0.938	0.052	0.920
S401	0.107	1.062	0.010	0.683	0.944	0.109
S069	0.107	0.893	0.010	0.859
S078	0.104	1.021	0.000	1.016	0.823	0.128
S257	0.104	1.620	0.016	1.348	1.287	0.112
S400	0.104	0.915	0.031	0.811	0.851	0.109
S492	0.102	0.754	0.010	0.698	0.874	0.086
S412	0.099	0.782	0.026	0.672	0.968	0.073
S090	0.096	0.809	0.026	0.610	0.697	0.109
S322	0.096	0.690	0.016	0.599	0.582	0.107
S049	0.096	0.968	0.042	0.990	1.094	0.097
S406	0.096	0.748	0.010	0.537	0.807	0.086
S293	0.096	0.965	0.031	1.032	0.812	0.081
S170	0.094	0.970	0.032	0.891	0.875	0.110
S451	0.094	0.884	0.021	0.816	0.760	0.115
S012	0.094	0.928	0.016	0.699	0.746	0.104
S029	0.094	0.924	0.068	0.875
S327	0.086	0.933	0.005	0.690	1.036	0.096

Reaction Time Distribution

plot_distribution <- function(data) {
  data |>
    estimate_density(select = "RT", at = c("Participant", "Task", "Block")) |>
    group_by(Participant) |>
    normalize(select = "y") |>
    ungroup() |>
    mutate(
      # Participant = fct_relevel(Participant, as.character(table$Participant)),
      color = case_when(
        Participant %in% outliers_perceptual & Task == "Perceptual" ~ "red",
        Participant %in% outliers_illusion1 & Task == "Illusion_Session1" ~ "red",
        Participant %in% outliers_illusion2 & Task == "Illusion_Session2" ~ "red",
        Participant %in% outliers_perceptual_block2 & Task == "Perceptual" ~ "orange",
        Participant %in% outliers_illusion1_block2 & Task == "Illusion_Session1" ~ "orange",
        TRUE ~ "blue"
      ),
      Task = fct_recode(Task,
        "Illusion task (session 1)" = "Illusion_Session1",
        # "Illusion task (session 2)" = "Illusion_Session2",
        "Perceptual task" = "Perceptual"
      )
    ) |>
    ggplot(aes(x = x, y = y)) +
    geom_area(data = normalize(estimate_density(data, select = "RT"), select = "y"), alpha = 0.2) +
    geom_line(aes(color = color, group = interaction(Participant, Block), linetype = Block), size = 0.5) +
    # geom_vline(xintercept = 0.125, linetype = "dashed", color = "red", size = 0.5) +
    scale_color_manual(values = c("red" = "#F44336", "orange" = "#FF9800", "blue" = "blue"), guide = "none") +
    scale_x_continuous(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    coord_cartesian(xlim = c(0, 2)) +
    theme_modern() +
    theme(axis.text.y = element_blank(),
          axis.line.y = element_blank(),
          strip.text.y = element_text(size = rel(0.6))) +
    facet_grid(Participant ~ Task) +
    labs(y = "", x = "Reaction Time (s)")
}

p <- list()
for(i in 1:5) {
  p[[i]] <- data |>
  filter((as.numeric(gsub("\\D", "", Participant)) > (i - 1) * 100) & (as.numeric(gsub("\\D", "", Participant)) <= 100 * i)) |>
  plot_distribution()
}
p <- patchwork::wrap_plots(p, ncol=5, nrow=1) + plot_layout(guides = "collect")
ggsave("figures/1_Preprocessing-p_outliers_RT.png", p, width = 7*5, height = 24, dpi = 150)

knitr::include_graphics("figures/1_Preprocessing-p_outliers_RT.png")

illusion1 <- filter(illusion1, !Participant %in% outliers_illusion1)
illusion1 <- filter(illusion1, !(Participant %in% outliers_illusion1_block2 & Block == 2))
illusion2 <- filter(illusion2, !Participant %in% outliers_illusion2)
perceptual <- filter(perceptual, !Participant %in% outliers_perceptual)
perceptual <- filter(perceptual, !(Participant %in% outliers_perceptual_block2 & Block == 2))

Outliers Detection (Blocks)

For each block, we computed the error rate and, if more than 50%, we discarded the whole block (as it likely indicates that instructions got mixed up, for instance participants were selecting the smaller instead of the bigger circle).

data <- rbind(illusion1, illusion2, perceptual) |>
  group_by(Participant, Task, Illusion_Type, Block) |>
  summarize(ErrorRate_per_block = sum(Error) / n()) |>
  ungroup() |>
  arrange(desc(ErrorRate_per_block))


data |>
  estimate_density(at = c("Task", "Illusion_Type", "Block"), method = "KernSmooth") |>
  ggplot(aes(x = x, y = y)) +
  geom_line(aes(color = Illusion_Type, linetype = Block)) +
  geom_vline(xintercept = 0.5, linetype = "dashed") +
  scale_x_continuous(expand = c(0, 0)) +
  scale_y_continuous(expand = c(0, 0)) +
  scale_color_manual(values = c("Ebbinghaus" = "#2196F3", "MullerLyer" = "#4CAF50", "VerticalHorizontal" = "#FF5722")) +
  labs(y = "Distribution", x = "Error Rate") +
  theme_modern() +
  facet_wrap(~Task)

remove_badblocks <- function(df) {
  n <- nrow(df)
  df <- df |>
    group_by(Participant, Illusion_Type, Block) |>
    mutate(ErrorRate_per_block = sum(Error) / n()) |>
    ungroup() |>
    filter(ErrorRate_per_block < 0.5) |>
    select(-ErrorRate_per_block)
  
  text <- paste0(
    "We removed ",
    n - nrow(df),
    " (",
    insight::format_value((n - nrow(df)) / n, as_percent = TRUE),
    ") trials belonging to bad blocks."
  )
  list(data = df, text = text)
}

out <- remove_badblocks(illusion1)
print(paste("Illusion (session 1):", out$text))

[1] “Illusion (session 1): We removed 704 (0.37%) trials belonging to bad blocks.”

illusion1 <- out$data

out <- remove_badblocks(illusion2)
print(paste("Illusion (session 2):", out$text))

[1] “Illusion (session 2): We removed 512 (0.30%) trials belonging to bad blocks.”

illusion2 <- out$data

out <- remove_badblocks(perceptual)
print(paste("Perceptual task:", out$text))

[1] “Perceptual task: We removed 544 (0.57%) trials belonging to bad blocks.”

perceptual <- out$data

Outliers Detection (Trials)

Reaction Time per Trial

check_trials <- function(df) {
  data <- df |>
    mutate(Outlier = ifelse(RT >= 10, TRUE, FALSE)) |>
    group_by(Participant) |>
    mutate(Outlier = ifelse(RT < 0.125 | standardize(RT, robust = TRUE) > 4, TRUE, Outlier)) |>
    ungroup()

  p1 <- data |>
    filter(RT < 10) |>
    estimate_density(select = "RT", at = "Participant") |>
    group_by(Participant) |>
    normalize(select = "y") |>
    ungroup() |>
    merge(data |>
      group_by(Participant) |>
      mutate(Threshold = median(RT) + 4 * mad(RT)) |>
      filter(Error == 0) |>
      summarize(Threshold = mean(Threshold))) |>
    mutate(Outlier = ifelse(x >= Threshold, TRUE, FALSE)) |>
    ggplot(aes(x = x, y = y)) +
    geom_area(data = normalize(estimate_density(filter(data, RT < 10), select = "RT"), select = "y"), alpha = 0.2) +
    geom_line(aes(color = Participant, linetype = Outlier), alpha = 0.2) +
    geom_vline(xintercept = c(125), linetype = "dashed", color = "red") +
    scale_color_material_d("rainbow", guide = "none") +
    scale_x_continuous(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    guides(linetype = "none") +
    coord_cartesian(xlim = c(0, 5)) +
    theme_modern() +
    theme(axis.text.y = element_blank()) +
    labs(y = "", x = "Reaction Time (s)")


  p2 <- data |>
    group_by(Participant) |>
    summarize(Outlier = sum(Outlier) / nrow(illusion1)) |>
    mutate(Participant = fct_reorder(Participant, Outlier)) |>
    ggplot(aes(x = Participant, y = Outlier)) +
    geom_bar(stat = "identity", aes(fill = Participant)) +
    scale_fill_material_d("rainbow", guide = "none") +
    scale_x_discrete(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0), labels = scales::percent) +
    see::theme_modern() +
    theme(axis.text.x = element_blank()) +
    labs(y = "Percentage of outlier trials")

  text <- paste0(
    "We removed ",
    sum(data$Outlier),
    " (",
    insight::format_value(sum(data$Outlier) / nrow(data), as_percent = TRUE),
    ") outlier trials (125 ms < RT < 4 MAD above median)."
  )

  data <- filter(data, Outlier == FALSE)
  data$Outlier <- NULL

  list(p = p1 / p2, data = data, text = text)
}

Illusion Task (Session 1)

out <- check_trials(illusion1)
print(paste("Illusion (session 1):", out$text))

[1] “Illusion (session 1): We removed 6735 (3.55%) outlier trials (125 ms < RT < 4 MAD above median).”

out$p

illusion1 <- out$data

Illusion Task (Session 2)

out <- check_trials(illusion2)
print(paste("Illusion (session 2):", out$text))

[1] “Illusion (session 2): We removed 6009 (3.50%) outlier trials (125 ms < RT < 4 MAD above median).”

out$p

illusion2 <- out$data

Perceptual Task

out <- check_trials(perceptual)
print(paste("Perceptual task:", out$text))

[1] “Perceptual task: We removed 4664 (4.92%) outlier trials (125 ms < RT < 4 MAD above median).”

out$p

perceptual <- out$data

Outlier Detection (Questionnaires)

Multivariate Detection

We compute a multivariate outlier score.

outliers <- sub |> 
  select(AttentionCheck_Session1, 
         IPIP6_RT, PID5_RT, GCBS_RT, ASQ_RT, LIE_RT, SPQ_RT, 
         IPIP6_SD, PID5_SD, GCBS_SD, ASQ_SD, LIE_SD, SPQ_SD, 
         PHQ4_SD) |> 
  standardize() |> 
  performance::check_outliers(method = c("mahalanobis", "mahalanobis_robust", "mcd", "ics"))

sub$Outlier_Questionnaire <- as.data.frame(outliers)$Outlier
# outliers

Manual Check

# < 0.80 to the average attention check and 1 in outlier score
outliers_questionnaires <- c(
  "S003", "S008", "S047", "S068", "S110", "S127", "S151", "S189", "S261", "S266", "S390", "S489", "S494"
)

We removed the questionnaire data from 13 participants upon inspection of attention checks and time taken to complete each questionnaires.

table <- sub |>
  mutate(
    Outlier_Task1 = Participant %in% outliers_illusion1,
    Outlier_Task1b = Participant %in% outliers_illusion1_block2 / 2,
    Outlier_Task2 = Participant %in% outliers_illusion2,
    Outlier_Task3 = Participant %in% outliers_perceptual,
    Outlier_Task3b = Participant %in% outliers_perceptual_block2 / 2,
    Outlier_Tasks = Outlier_Task1 + Outlier_Task1b + 
      Outlier_Task2 + 
      Outlier_Task3 + Outlier_Task3b
  ) |>
  select(
    Participant,
    Outlier_Tasks,
    Outlier_Questionnaire,
    AttentionCheck_Session1,
    IPIP6_RT, PID5_RT, ASQ_RT, SPQ_RT,
    IPIP6_SD, PID5_SD, PHQ4_SD,
    AttentionCheck_Session2,
    BPD_RT, MAIA_RT, PI_RT,
    BPD_SD, MAIA_SD, PI_SD
  ) |> 
  # mutate(across(ends_with("IPIP6_RT") | ends_with("IPIP6_SD"), standardize)) |> 
  # arrange(desc(Outlier_Tasks), AttentionCheck_Session1) 
  arrange(desc(Participant))

t <- data.frame(Participant = c("Average"), t(sapply(table[2:ncol(table)], mean, na.rm = TRUE))) |>
  rbind(table) |>
  knitr::kable() |>
  kableExtra::row_spec(1, italic = TRUE) |>
  kableExtra::row_spec(which(table$Participant %in% outliers_questionnaires) + 1, background = "#EF9A9A")

for (i in 2:ncol(table)) {
  t <- kableExtra::column_spec(
    t, i,
    color = "white",
    background = kableExtra::spec_color(
      c(NA, table[[i]]),
      option = "D",
      alpha = 1,
      # direction = ifelse(str_detect(names(table)[i], "_SD|Outlier"), 1, -1),
      na_color = "white",
    )
  )
}


t  |>
  kableExtra::row_spec(1, background = "grey") |> 
  kableExtra::kable_styling(full_width = TRUE, font_size = 9) |>
  kableExtra::scroll_box(width = "100%", height = "500px")

Participant	Outlier_Tasks	Outlier_Questionnaire	AttentionCheck_Session1	IPIP6_RT	PID5_RT	ASQ_RT	SPQ_RT	IPIP6_SD	PID5_SD	PHQ4_SD	AttentionCheck_Session2	BPD_RT	MAIA_RT	PI_RT	BPD_SD	MAIA_SD	PI_SD
Average	0.025	0.147	0.966	1.757	2.518	2.644	3.086	0.191	0.679	0.397	0.987	1.306	3.586	3.560	0.257	0.142	0.825
S500	0.000	0.000	1.000	2.152	2.515	2.119	3.357	0.080	0.589	0.354	1.000	1.338	3.939	4.077	0.078	0.064	0.556
S499	0.000	0.000	1.000	1.358	1.247	1.449	2.843	0.141	0.597	0.000	1.000	1.404	1.909	1.935	0.043	0.151	0.789
S498	0.000	0.000	1.000	1.410	2.037	2.403	3.236	0.261	0.780	0.354	1.000	1.955	3.037	3.718	0.386	0.136	1.304
S497	0.000	0.500	1.000	1.334	4.179	1.574	2.262	0.274	0.974	0.707	1.000	1.128	2.601	3.026	0.188	0.150	0.552
S496	0.000	0.000	1.000	2.285	1.544	1.588	1.692	0.240	0.769	0.707	1.000	0.980	2.365	2.782	0.323	0.272	0.734
S495	0.000	0.000	0.939	1.070	1.668	1.902	2.171	0.175	0.817	0.354	1.000	0.911	2.016	3.118	0.218	0.094	0.772
S494	0.000	0.500	0.668	1.350	0.996	1.223	1.337	0.211	0.508	0.000	0.710	0.961	1.519	1.759	0.253	0.066	0.995
S493	0.000	0.000	1.000	0.855	1.174	1.286	1.566	0.136	0.713	0.000	1.000	0.792	1.758	2.067	0.297	0.142	0.445
S492	0.000	0.000	1.000	1.815	1.971	2.290	2.577	0.166	0.597	0.000	1.000	1.057	2.473	2.588	0.295	0.155	0.755
S491	0.000	0.000	1.000	1.547	1.894	3.160	3.661	0.178	0.309	0.354	1.000	1.101	4.649	4.824	0.076	0.173	0.995
S490	0.000	0.000	1.000	1.008	1.660	1.690	1.944	0.168	0.933	0.000	1.000	0.646	2.321	2.307	0.234	0.167	0.773
S489	0.000	0.500	0.772	1.425	1.728	1.942	2.380	0.296	0.597	1.061	0.910	0.944	2.591	2.818	0.210	0.109	0.628
S488	0.000	0.000	1.000	3.671	3.699	3.928	5.844	0.114	0.268	0.000
S487	0.000	0.250	0.799	1.148	1.954	2.538	2.899	0.150	0.288	0.354	0.863	0.983	2.871	3.307	0.245	0.105	0.500
S486	0.000	0.000	1.000	1.289	2.222	2.475	2.942	0.206	0.753	0.354	1.000	0.915	3.154	3.167	0.264	0.163	0.825
S485	0.000	0.000	1.000	0.985	1.331	1.218	1.556	0.148	0.793	0.000	1.000	1.093	2.073	3.135	0.407	0.150	0.642
S484	0.000	0.000	1.000	1.103	1.128	1.778	1.908	0.147	0.219	0.000	1.000	0.610	2.373	2.760	0.078	0.100	0.700
S483	0.000	0.000	1.000	1.078	1.528	2.180	2.185	0.096	0.559	0.000	1.000	0.978	2.504	2.734	0.310	0.105	0.499
S482	0.000	0.000	0.893	2.017	2.087	2.301	2.726	0.175	0.825	0.000	1.000	0.692	2.663	2.723	0.105	0.107	0.961
S481	0.000	0.000	1.000	1.491	1.903	1.921	2.422	0.221	0.623	0.354	1.000	0.885	2.998	3.084	0.322	0.139	0.913
S480	0.000	0.500	0.994	1.217	2.055	1.730	2.726	0.145	0.820	0.354	1.000	1.132	2.509	2.880	0.208	0.125	0.842
S479	0.000	0.000	1.000	2.022	2.098	2.393	3.819	0.109	0.450	0.354
S478	0.000	0.000	1.000	1.020	1.765	2.004	2.980	0.081	0.528	0.000	1.000	1.093	1.997	2.424	0.239	0.034	0.383
S477	0.000	0.000	1.000	1.345	2.031	3.069	2.267	0.177	0.288	0.000	1.000	1.031	2.566	2.560	0.079	0.129	1.002
S476	0.000	0.000	1.000	1.173	1.378	1.441	1.822	0.216	0.854	0.707	1.000	1.840	1.781	2.437	0.171	0.130	0.726
S475	0.000	0.000	1.000	3.121	2.987	3.611	3.808	0.248	0.358	0.354
S474	0.000	0.000	1.000	2.046	3.418	2.702	4.368	0.102	0.597	0.354	1.000	1.440	2.877	3.341	0.235	0.094	0.519
S473	0.000	0.000	0.964	1.721	2.013	2.245	3.805	0.189	0.636	0.354	1.000	1.400	2.935	3.071	0.366	0.176	0.694
S472	0.000	0.000	0.996	1.055	1.887	1.906	2.766	0.253	0.892	0.707	1.000	1.543	3.843	3.068	0.233	0.082	0.502
S471	0.000	0.000	1.000	1.194	2.236	2.027	3.571	0.128	0.733	1.061
S470	0.000	1.000	0.906	1.657	2.900	36.735	3.249	0.192	0.765	0.000	1.000	1.486	3.899	4.254	0.088	0.136	0.852
S469	0.000	0.500	0.929	1.936	4.377	2.366	4.967	0.194	0.939	0.000	1.000	1.223	2.507	5.538	0.231	0.315	1.205
S468	0.000	0.000	1.000	1.721	2.148	2.389	3.304	0.106	0.802	0.000
S467	0.000	0.500	0.981	3.028	5.796	5.871	6.819	0.067	0.577	0.354	1.000	1.424	4.669	3.812	0.181	0.037	0.571
S466	0.000	0.000	0.929	0.992	1.659	1.935	2.350	0.117	0.999	0.707	1.000	0.703	1.488	1.945	0.389	0.213	0.938
S465	0.000	0.500	1.000	2.655	6.778	2.986	3.717	0.121	0.686	0.354	1.000	1.953	3.967	4.020	0.371	0.090	0.747
S464	0.000	0.000	1.000	1.346	1.734	1.858	2.873	0.164	0.822	0.354	0.993	0.879	3.021	2.574	0.129	0.181	0.863
S463	0.000	0.500	1.000	1.144	2.580	4.053	3.803	0.170	0.736	0.707	1.000	0.786	1.300	1.915	0.256	0.075	0.669
S462	0.000	0.500	1.000	2.981	3.195	3.622	5.789	0.181	0.597	0.000	1.000	3.737	5.439	5.132	0.258	0.215	1.263
S461	0.000	0.000	0.996	1.627	1.803	1.882	2.838	0.224	0.675	0.000	1.000	0.811	1.839	2.267	0.217	0.138	1.072
S460	0.000	0.000	1.000	0.973	1.492	2.015	2.327	0.170	0.880	0.707	1.000	1.892	5.263	4.954	0.262	0.075	0.979
S459	0.000	0.500	0.857	2.207	4.385	3.471	4.551	0.372	0.750	0.707	1.000	2.021	4.535	4.174	0.314	0.113	0.568
S458	0.000	0.000	1.000	1.413	1.572	1.795	2.028	0.212	0.366	0.000	1.000	0.878	2.308	2.953	0.238	0.131	0.764
S457	0.000	0.000	0.964	1.023	1.508	1.613	1.913	0.292	0.598	0.354	1.000	0.654	2.160	2.092	0.298	0.137	0.555
S456	0.000	0.000	1.000	1.802	2.127	2.273	3.147	0.183	0.539	0.354	1.000	1.527	13.744	4.213	0.302	0.161	1.009
S455	0.000	0.000	1.000	1.523	2.928	3.872	3.580	0.323	0.589	0.000	1.000	1.277	2.688	3.344	0.412	0.134	0.950
S454	0.000	0.000	1.000	1.151	1.839	1.537	1.972	0.182	0.704	0.000	1.000	1.027	2.664	2.815	0.308	0.168	0.689
S453	0.000	0.000	1.000	0.649	1.024	1.211	1.384	0.137	0.358	0.000	1.000	0.679	1.980	1.509	0.025	0.151	0.896
S452	0.000	0.000	1.000	0.916	2.441	1.401	1.721	0.096	0.386	0.707	1.000	0.670	1.703	2.028	0.170	0.085	0.767
S451	0.000	0.000	0.999	1.394	1.544	1.628	2.044	0.153	0.199	0.000	1.000	0.940	2.514	2.360	0.103	0.157	0.941
S450	0.000	0.000	1.000	1.055	1.365	1.887	2.397	0.178	0.398	0.000	1.000	0.846	3.244	2.893	0.193	0.106	0.832
S449	0.000	0.500	0.964	1.876	5.459	2.893	5.794	0.373	0.467	0.000	1.000	5.481	3.527	9.812	0.006	0.162	1.149
S448	0.000	0.000	0.857	1.176	2.117	2.249	1.827	0.186	0.398	0.354
S447	0.000	0.000	1.000	1.985	1.919	2.338	2.526	0.157	0.635	0.000	1.000	1.103	2.903	3.738	0.293	0.098	0.869
S446	0.000	0.750	0.929	2.662	9.281	9.664	5.270	0.181	0.591	0.000	1.000	2.181	8.876	6.374	0.069	0.174	0.760
S445	0.000	0.000	0.993	1.170	1.639	1.490	2.015	0.138	0.701	0.000	1.000	0.987	2.824	2.554	0.237	0.117	0.778
S444	0.000	0.000	0.964	0.832	0.937	2.017	2.812	0.233	0.509	0.000	1.000	3.185	7.219	2.327	0.200	0.182	0.962
S443	0.000	0.000	1.000	1.646	1.581	2.470	2.658	0.270	0.508	0.000	1.000	1.308	3.273	3.547	0.373	0.187	0.999
S442	0.000	0.000	1.000	1.367	1.844	2.285	2.524	0.205	0.288	0.000	1.000	0.911	3.842	3.841	0.000	0.159	1.316
S441	0.000	0.000	1.000	0.908	1.274	1.271	1.951	0.215	0.609	0.707	1.000	0.767	2.908	2.332	0.271	0.179	0.972
S440	0.000	0.500	0.847	3.895	4.232	4.473	4.230	0.170	0.646	0.354	0.817	3.404	7.799	7.163	0.383	0.138	1.299
S439	0.000	0.000	0.909	1.037	1.485	1.395	3.111	0.187	0.816	0.707	0.767	0.535	2.446	1.601	0.223	0.187	1.336
S438	0.000	0.000	0.909	2.666	3.005	3.875	3.239	0.112	0.398	0.000	0.880	3.762	3.565	7.861	0.137	0.108	0.443
S437	0.000	0.000	0.964	2.567	3.659	3.170	4.034	0.112	0.791	0.000	1.000	1.234	2.880	2.929	0.226	0.154	0.891
S436	0.000	0.000	1.000	2.059	2.682	2.929	4.007	0.155	0.656	0.707	1.000	1.010	2.895	2.867	0.322	0.122	0.566
S435	0.000	0.750	1.000	5.149	1.818	2.264	3.842	0.195	0.597	0.000	1.000	1.114	4.249	4.067	0.316	0.099	0.828
S434	0.000	0.000	1.000	1.417	1.668	1.900	2.097	0.207	0.268	0.000	1.000	0.841	2.511	2.468	0.099	0.134	0.984
S433	0.000	0.000	1.000	1.977	2.041	3.603	2.873	0.164	0.089	1.061	1.000	1.163	3.563	3.115	0.025	0.163	0.532
S432	0.000	0.500	1.000	5.170	6.957	5.273	6.912	0.176	0.548	0.707	0.810	2.475	5.380	8.876	0.239	0.090	0.621
S431	0.000	0.000	0.929	2.082	3.009	2.810	4.377	0.232	0.701	0.000	1.000	1.267	5.396	3.948	0.230	0.181	0.713
S430	0.000	0.000	1.000	2.275	1.550	2.438	2.225	0.154	0.594	0.707	1.000	1.024	2.341	2.646	0.244	0.170	0.994
S429	0.000	0.000	0.929	1.773	2.629	2.549	3.425	0.111	0.597	0.354	1.000	0.948	2.451	2.922	0.125	0.066	0.575
S428	0.000	0.000	1.000	1.353	1.715	2.391	2.245	0.225	0.756	0.354	1.000	0.879	3.193	3.555	0.355	0.132	0.808
S427	0.000	0.000	0.964	0.988	1.179	1.343	1.797	0.198	0.802	0.354	1.000	0.720	1.664	1.708	0.406	0.101	0.731
S426	0.000	0.000	1.000	2.800	3.298	2.952	4.105	0.160	0.770	0.707
S425	0.000	0.000	0.964	1.218	1.852	1.799	2.402	0.247	0.822	0.707	1.000	0.979	3.038	3.228	0.244	0.130	0.705
S424	0.000	0.000	1.000	1.035	1.345	1.719	1.955	0.136	0.470	0.000	0.993	0.552	2.213	2.089	0.106	0.137	0.890
S423	0.000	0.000	1.000	1.996	2.617	2.397	3.507	0.232	0.976	0.354	1.000	1.423	3.519	3.337	0.303	0.154	0.982
S422	0.000	0.500	1.000	0.930	1.088	3.206	1.474	0.204	0.744	0.000	1.000	0.610	2.195	2.083	0.454	0.296	1.183
S421	0.000	0.000	1.000	1.921	2.370	2.797	2.852	0.210	0.637	0.000	1.000	1.127	2.055	3.401	0.214	0.149	0.873
S420	0.000	0.250	1.000	1.693	3.918	2.396	2.771	0.180	0.900	0.354	0.850	0.687	1.991	2.675	0.113	0.106	0.880
S419	0.000	0.000	0.927	2.243	2.959	3.137	4.554	0.175	0.747	0.707	0.917	1.936	4.657	5.720	0.233	0.057	1.303
S418	0.000	0.000	0.929	2.518	2.519	3.710	4.558	0.336	0.591	0.707	1.000	1.012	2.727	3.081	0.373	0.180	0.851
S417	0.000	0.500	0.893	1.544	3.196	3.891	3.337	0.261	0.577	0.354	1.000	1.416	3.078	3.923	0.359	0.133	1.029
S416	0.000	0.000	0.929	1.998	2.787	3.576	3.767	0.244	0.488	0.354	1.000	1.276	3.914	5.069	0.155	0.108	1.036
S415	0.000	0.000	1.000	2.190	2.475	2.966	3.830	0.173	0.704	0.000	1.000	1.732	3.120	5.146	0.349	0.088	0.472
S414	0.000	0.500	1.000	3.295	6.307	3.120	4.905	0.210	0.862	0.000
S413	0.000	0.000	1.000	2.029	2.585	2.423	4.052	0.073	0.565	0.354	1.000	3.060	8.715	8.385	0.274	0.092	0.773
S412	0.000	0.000	0.964	0.988	1.339	1.798	2.039	0.144	0.881	1.061	1.000	0.944	2.271	2.076	0.304	0.133	0.700
S411	0.000	0.500	0.857	1.541	2.198	2.846	5.589	0.353	0.977	0.707	0.933	2.433	3.115	14.687	0.422	0.129	1.178
S410	0.000	0.500	0.860	1.377	3.288	3.352	4.919	0.202	0.790	0.354	0.923	0.957	2.609	3.106	0.339	0.220	0.627
S409	0.000	0.000	1.000	1.403	1.948	2.047	2.448	0.282	0.436	0.000	1.000	1.176	2.417	2.677	0.465	0.166	0.749
S408	0.000	0.500	0.964	1.233	2.487	2.292	4.261	0.103	0.424	0.707	1.000	1.150	3.305	3.969	0.061	0.114	0.730
S407	0.000	0.000	0.821	0.754	1.716	1.718	1.973	0.178	0.796	0.354	1.000	1.124	3.344	2.966	0.409	0.171	0.846
S406	0.000	0.000	1.000	0.764	1.010	1.244	1.949	0.099	0.539	0.354	1.000	0.453	1.204	1.102	0.223	0.039	0.512
S405	0.000	0.000	1.000	1.243	1.461	2.238	2.399	0.093	0.179	0.354	1.000	0.800	2.515	2.702	0.278	0.129	0.555
S404	0.000	0.000	1.000	1.753	2.605	2.569	2.319	0.202	0.864	0.000	1.000	0.876	6.610	2.792	0.344	0.134	0.938
S403	0.000	0.000	0.929	0.835	1.029	1.006	1.488	0.158	0.577	0.354	1.000	0.473	1.621	1.419	0.114	0.212	0.761
S402	0.000	0.000	0.964	1.112	1.384	1.688	1.892	0.198	0.650	0.354	0.547	0.239	1.233	0.771	0.120	0.072	1.035
S401	0.000	0.000	1.000	1.543	2.280	2.172	2.323	0.130	0.578	0.354	1.000	0.477	3.095	3.418	0.228	0.179	1.218
S400	0.000	0.000	0.964	2.461	3.903	3.167	4.653	0.196	0.618	0.000	1.000	1.198	4.221	2.750	0.282	0.051	0.455
S399	0.000	0.000	1.000	2.039	4.241	2.835	3.381	0.217	0.643	0.354	1.000	1.466	4.429	4.071	0.239	0.107	0.534
S398	0.000	0.000	0.964	0.987	1.159	1.480	1.519	0.201	0.793	0.354	1.000	0.869	2.416	1.951	0.390	0.265	0.897
S397	0.000	0.500	1.000	2.362	2.901	2.980	3.754	0.220	0.779	0.000	1.000	1.459	4.625	3.992	0.328	0.118	1.184
S396	0.000	0.000	1.000	1.049	1.383	1.547	1.869	0.141	0.589	0.354	1.000	0.725	2.609	2.087	0.212	0.125	0.554
S395	0.000	0.000	1.000	0.995	1.213	1.526	1.579	0.256	0.790	0.354	1.000	0.783	2.053	2.292	0.358	0.242	0.716
S394	0.000	0.000	0.964	1.983	2.782	3.075	3.496	0.149	0.684	1.414	1.000	1.571	3.676	3.356	0.276	0.107	0.897
S393	0.000	0.500	1.000	3.238	3.484	3.482	3.599	0.274	1.128	0.354	1.000	1.477	7.221	5.721	0.285	0.176	0.794
S392	0.000	0.000	1.000	0.880	1.104	1.464	1.815	0.108	0.358	0.000	1.000	0.451	1.915	1.845	0.073	0.093	0.276
S391	0.000	1.000	0.883	1.963	2.519	16.085	5.164	0.215	0.557	0.000	0.663	0.651	0.773	0.817	0.127	0.019	0.655
S390	0.000	0.750	0.797	1.068	1.744	2.132	1.173	0.036	0.000	0.000	0.693	0.380	6.526	0.895	0.005	0.012	0.500
S389	1.000	0.000	0.883	0.976	1.244	1.499	0.891	0.209	0.745	1.061
S388	0.000	0.000	1.000	1.006	1.015	1.373	1.528	0.246	0.946	0.354	1.000	0.501	1.466	1.879	0.374	0.317	0.998
S387	0.000	0.000	0.881	1.398	2.335	2.165	2.453	0.276	0.894	0.000	1.000	1.151	3.732	2.777	0.527	0.322	0.941
S386	0.000	0.000	0.809	1.403	2.314	2.364	2.474	0.136	0.615	0.354
S385	0.000	0.000	0.883	2.021	1.864	2.012	2.422	0.182	0.655	0.354	1.000	1.121	3.035	3.123	0.301	0.215	0.754
S384	0.000	1.000	1.000	4.145	7.418	9.643	6.283	0.155	0.656	0.354	1.000	1.832	5.397	8.458	0.212	0.080	0.381
S383	0.000	0.000	0.857	2.025	2.833	3.001	3.498	0.296	0.905	0.354	1.000	2.058	4.778	4.878	0.194	0.114	1.035
S382	0.000	0.000	1.000	1.322	1.865	2.084	2.254	0.208	0.675	0.354
S381	0.000	0.000	1.000	1.146	1.984	2.006	2.622	0.141	0.765	0.354	1.000	1.497	3.209	3.201	0.085	0.088	0.505
S380	0.000	0.000	0.957	1.471	1.633	1.946	2.817	0.146	0.629	0.000	0.940	1.310	3.383	3.903	0.184	0.134	0.728
S379	0.000	0.000	1.000	2.965	2.272	3.183	3.625	0.232	0.985	0.707	1.000	1.595	3.764	3.774	0.377	0.183	0.877
S378	0.000	0.000	1.000	2.425	4.388	3.619	5.145	0.117	0.815	0.707	1.000	1.931	4.079	3.775	0.188	0.117	0.940
S377	0.000	0.500	1.000	2.427	3.722	5.139	5.178	0.264	0.487	0.000	1.000	2.097	5.058	5.802	0.189	0.058	0.516
S376	0.000	0.000	0.999	1.006	1.669	1.764	2.052	0.193	0.843	0.707	1.000	1.029	3.448	2.459	0.302	0.258	0.469
S375	0.000	0.000	1.000	1.140	1.401	1.622	2.051	0.210	0.508	0.354	0.803	0.760	2.109	1.946	0.192	0.045	0.637
S374	0.000	0.000	0.964	0.833	1.678	2.215	2.446	0.147	0.585	0.707	1.000	0.768	1.533	1.930	0.171	0.128	0.532
S373	0.000	0.000	1.000	2.300	2.868	4.121	4.823	0.235	0.537	1.061	1.000	2.338	5.171	5.014	0.500	0.172	1.364
S372	0.000	0.000	1.000	1.204	1.542	1.681	1.919	0.208	0.597	0.707	1.000	1.238	3.323	2.833	0.414	0.088	0.466
S371	0.000	0.000	1.000	1.009	1.448	1.591	1.989	0.097	0.450	0.354	1.000	1.271	2.274	2.728	0.322	0.184	0.701
S370	0.000	0.500	1.000	1.112	2.344	3.959	2.988	0.157	0.736	0.354	1.000	0.902	1.916	2.350	0.118	0.153	0.581
S369	0.000	0.500	1.000	3.974	2.381	3.692	2.903	0.273	1.059	0.354	1.000	2.087	3.867	4.850	0.418	0.240	1.144
S368	0.000	1.000	0.964	3.890	5.642	3.103	7.225	0.201	0.669	0.354	1.000	1.323	3.342	7.264	0.099	0.150	0.639
S367	0.000	0.000	0.817	1.543	1.853	2.402	2.799	0.086	0.545	0.000	0.923	0.916	2.337	2.688	0.126	0.109	0.575
S366	0.000	0.500	0.800	0.945	2.117	0.863	2.667	0.159	0.649	0.707
S365	0.000	0.250	0.914	2.834	2.259	2.029	3.803	0.167	0.918	1.061	1.000	1.367	3.362	2.871	0.099	0.236	1.173
S364	0.000	0.000	1.000	1.367	2.460	2.138	2.805	0.163	0.876	1.414	1.000	0.783	2.430	2.457	0.270	0.156	0.934
S363	0.000	0.000	1.000	1.316	1.810	2.432	3.296	0.314	0.687	0.707	0.910	0.820	2.979	3.748	0.327	0.142	1.117
S362	0.000	0.000	1.000	1.043	1.944	2.111	2.449	0.220	0.814	0.707	1.000	0.756	2.206	3.401	0.243	0.173	0.751
S361	0.000	0.500	0.929	1.597	2.361	2.018	2.899	0.218	0.710	0.000	1.000	1.111	4.059	2.108	0.229	0.046	1.172
S360	0.000	0.000	1.000	1.003	1.322	1.522	1.716	0.208	1.339	0.000
S359	0.000	0.000	1.000	1.102	2.096	2.132	2.835	0.116	0.626	0.354	1.000	0.857	2.247	3.112	0.239	0.084	0.740
S358	0.000	0.250	1.000	2.137	1.879	2.210	3.424	0.184	0.110	0.707	1.000	1.546	3.408	3.415	0.346	0.145	0.872
S357	0.000	0.000	1.000	1.873	1.757	1.450	2.224	0.122	0.736	0.354	1.000	1.519	4.134	4.086	0.241	0.152	0.917
S356	0.000	0.500	0.964	1.638	8.675	2.701	3.368	0.161	0.984	0.000	1.000	0.836	2.447	2.905	0.227	0.152	0.583
S355	0.000	1.000	0.857	7.354	16.200	6.168	5.644	0.217	1.067	0.354	1.000	6.054	10.720	8.264	0.354	0.200	0.808
S354	0.000	0.000	1.000	1.877	1.896	1.915	2.729	0.177	0.832	0.000	1.000	1.081	2.324	2.877	0.082	0.161	0.934
S353	0.000	0.000	1.000	1.102	1.872	1.743	2.106	0.189	0.704	0.707	1.000	1.152	2.310	2.855	0.200	0.135	0.613
S352	0.000	0.000	1.000	1.802	2.610	2.557	3.432	0.148	0.915	0.000	1.000	1.704	3.716	3.452	0.260	0.157	0.678
S351	0.000	0.000	1.000	1.842	1.479	1.659	2.113	0.233	0.736	0.354	1.000	0.742	1.885	2.352	0.423	0.226	1.142
S350	1.000	0.500	0.809	2.331	0.834	1.098	1.506	0.149	0.528	0.354	1.000	0.968	2.221	3.033	0.237	0.143	0.526
S349	0.000	0.000	1.000	2.419	2.293	2.330	2.801	0.102	0.398	0.000	1.000	0.910	3.294	3.041	0.231	0.034	0.502
S348	0.000	0.000	1.000	2.404	3.883	3.435	4.724	0.195	1.026	1.061	1.000	2.188	4.622	6.162	0.268	0.207	1.110
S347	0.000	0.000	0.909	1.811	2.162	2.394	3.429	0.322	0.794	0.707	1.000	1.229	3.485	3.235	0.398	0.221	1.306
S346	0.000	0.000	0.917	0.979	1.242	1.324	1.589	0.292	1.061	0.354	1.000	0.595	4.135	1.863	0.426	0.262	0.777
S345	0.000	0.500	1.000	1.751	3.694	3.467	2.877	0.191	0.618	0.354	1.000	2.532	4.349	3.945	0.316	0.124	0.913
S344	0.000	0.000	1.000	1.572	3.368	2.547	3.644	0.244	0.822	1.414	1.000	1.207	3.411	3.733	0.114	0.286	0.868
S343	0.000	0.000	1.000	2.303	2.798	3.598	3.459	0.222	0.786	0.707	1.000	1.475	3.919	4.263	0.137	0.158	0.437
S342	0.000	0.000	1.000	1.699	2.692	2.310	3.125	0.262	0.793	0.000
S341	0.000	0.750	1.000	2.313	4.555	4.486	3.517	0.359	1.429	0.000	1.000	3.608	8.927	4.242	0.105	0.187	0.627
S340	0.000	0.500	0.929	1.787	1.880	3.759	4.951	0.218	0.963	0.354	1.000	1.135	2.782	2.809	0.287	0.198	1.273
S339	0.000	0.000	1.000	1.064	2.105	1.743	2.153	0.158	0.843	0.707
S338	0.000	0.000	0.993	1.384	1.528	1.529	2.685	0.126	0.875	0.707	1.000	0.975	2.891	3.296	0.370	0.111	0.811
S337	0.000	0.000	1.000	1.037	1.266	1.436	1.760	0.228	0.725	0.354
S336	0.000	0.000	1.000	1.476	2.025	2.363	3.170	0.170	0.822	0.000	1.000	1.358	3.550	3.490	0.327	0.156	0.887
S335	0.000	0.000	1.000	0.712	1.284	1.842	2.818	0.110	0.358	0.354	1.000	0.457	1.293	1.965	0.192	0.094	0.658
S334	0.000	0.500	0.964	1.208	2.237	3.518	1.825	0.152	0.580	0.000	1.000	0.687	3.520	2.274	0.225	0.101	0.629
S333	0.000	0.000	1.000	1.202	1.185	1.561	1.624	0.109	0.843	0.354	1.000	0.665	1.804	1.946	0.098	0.123	0.982
S332	0.000	0.000	1.000	1.316	1.989	1.853	2.264	0.160	0.752	0.000	1.000	0.948	2.640	3.151	0.390	0.140	1.639
S331	0.000	0.500	1.000	1.112	3.467	2.818	2.065	0.202	0.854	0.707	1.000	0.675	2.090	3.311	0.347	0.160	1.159
S330	0.000	0.500	1.000	1.441	1.544	4.800	3.562	0.177	0.655	0.354	1.000	0.633	1.787	1.497	0.154	0.186	0.796
S329	0.000	0.000	1.000	2.855	2.610	3.528	4.327	0.150	0.666	0.707	1.000	1.486	3.750	5.909	0.159	0.112	0.651
S328	0.000	0.500	1.000	3.013	1.552	4.204	3.203	0.201	0.927	0.354	1.000	1.855	3.820	5.205	0.306	0.136	0.878
S327	0.000	0.000	1.000	2.116	3.950	3.017	3.482	0.178	1.020	0.000	1.000	1.684	4.275	4.425	0.402	0.035	0.365
S326	0.000	0.000	1.000	0.999	1.225	1.239	1.643	0.150	0.789	0.354	1.000	0.931	2.195	2.091	0.279	0.175	0.696
S325	0.000	0.500	1.000	1.329	1.863	1.876	2.395	0.204	0.487	0.000	1.000	1.040	9.196	4.648	0.195	0.095	0.519
S324	0.000	0.000	0.929	3.091	2.698	3.277	3.702	0.232	0.725	0.707	1.000	1.244	4.051	5.591	0.422	0.071	0.739
S323	0.000	0.000	1.000	0.954	1.318	1.337	1.440	0.139	0.398	0.354	1.000	0.726	2.389	2.010	0.145	0.116	0.625
S322	0.000	0.500	0.964	1.727	1.920	11.400	2.623	0.131	0.816	0.000	1.000	1.117	3.746	4.007	0.247	0.089	0.764
S321	0.000	0.000	1.000	1.994	2.965	3.001	3.601	0.174	0.508	0.000	1.000	1.938	5.051	4.693	0.213	0.104	0.499
S320	0.000	0.000	1.000	1.026	1.391	1.414	1.812	0.295	0.440	0.354	1.000	0.784	1.702	2.239	0.457	0.090	0.933
S319	0.000	0.000	1.000	1.316	1.822	1.462	2.878	0.186	0.869	0.000
S318	0.000	0.250	1.000	2.888	3.035	4.544	6.508	0.157	0.537	0.354
S317	0.000	0.000	1.000	0.957	1.183	1.579	1.584	0.132	0.509	0.707	1.000	0.774	1.583	1.645	0.157	0.088	0.389
S316	0.000	0.000	0.929	2.119	2.942	2.736	3.698	0.124	0.545	1.414	1.000	1.945	5.316	4.160	0.235	0.115	0.933
S315	0.000	0.000	0.964	2.642	2.798	2.771	3.306	0.110	0.705	0.354	1.000	1.677	4.617	5.350	0.400	0.210	1.077
S314	0.000	0.000	1.000	1.222	1.737	1.842	2.104	0.200	1.103	0.000	1.000	1.104	2.630	2.931	0.380	0.108	0.777
S313	0.000	0.000	0.964	1.260	1.770	1.902	2.423	0.168	0.718	1.768	1.000	1.173	3.499	3.183	0.378	0.172	0.744
S312	0.000	0.500	1.000	2.630	2.567	3.040	3.058	0.285	0.805	0.000	1.000	3.737	6.309	4.519	0.430	0.133	0.889
S311	0.000	0.000	0.964	2.117	2.602	2.534	4.364	0.167	0.701	0.354	1.000	2.299	5.688	5.300	0.380	0.083	0.846
S310	0.000	0.000	0.994	1.220	1.609	1.737	2.171	0.160	0.782	0.000	1.000	1.035	2.526	2.870	0.343	0.100	0.588
S309	0.000	0.000	1.000	1.624	2.438	3.253	3.039	0.230	0.733	0.354	1.000	0.864	2.357	3.219	0.290	0.125	1.150
S308	0.000	0.000	1.000	1.766	2.443	2.317	3.115	0.166	0.476	0.707	1.000	1.439	4.497	3.696	0.197	0.120	1.094
S307	0.000	0.000	1.000	1.573	2.674	2.665	4.045	0.209	0.901	0.707	1.000	1.799	4.107	4.457	0.236	0.190	1.677
S306	0.000	0.000	1.000	0.997	2.128	1.622	2.302	0.158	1.001	0.000	1.000	0.957	2.184	2.264	0.337	0.173	1.370
S305	0.000	0.000	0.821	2.060	2.704	3.046	3.275	0.250	0.858	0.354	1.000	1.472	3.238	3.844	0.134	0.108	0.924
S304	0.000	0.250	0.964	1.562	2.407	2.427	4.065	0.151	0.539	1.414	1.000	1.361	2.932	3.577	0.194	0.185	0.746
S303	0.000	1.000	0.857	1.155	15.210	5.229	2.523	0.199	0.776	0.707
S302	0.000	0.000	1.000	1.380	2.716	2.630	2.567	0.232	0.597	0.354	1.000	1.089	2.746	4.105	0.321	0.137	0.889
S301	0.000	0.000	1.000	1.924	2.567	2.683	3.260	0.175	0.943	0.707	1.000	0.967	2.667	3.555	0.287	0.189	0.756
S300	0.000	0.500	1.000	2.687	4.192	4.659	6.309	0.253	0.794	0.707	1.000	1.792	5.938	4.910	0.430	0.249	1.362
S299	0.000	0.500	0.929	1.506	2.880	1.561	1.693	0.212	0.372	0.000	1.000	0.726	2.085	2.734	0.000	0.153	1.188
S298	0.000	0.000	1.000	1.472	1.670	1.649	2.160	0.185	0.179	0.354	1.000	0.749	2.766	2.574	0.000	0.125	1.087
S297	0.000	0.000	1.000	1.394	1.852	1.771	2.433	0.177	0.456	0.000	1.000	1.108	2.670	2.660	0.113	0.075	0.578
S296	0.000	0.000	1.000	1.717	2.151	2.343	2.976	0.246	0.566	0.000	1.000	1.001	3.891	3.948	0.298	0.154	0.763
S295	0.000	0.500	0.857	2.627	2.803	2.288	2.554	0.160	0.655	0.707	1.000	1.079	3.459	3.033	0.133	0.087	0.722
S294	0.000	0.000	1.000	1.067	2.716	2.439	2.611	0.156	0.571	0.000	1.000	1.468	3.385	3.703	0.288	0.161	0.891
S293	0.000	0.000	1.000	1.049	1.290	1.278	1.958	0.162	0.410	0.354	1.000	1.040	2.397	2.558	0.293	0.136	0.787
S292	0.000	0.000	1.000	2.112	2.665	2.828	3.951	0.146	0.545	0.707	1.000	1.352	3.647	4.149	0.333	0.158	0.957
S291	0.000	0.500	1.000	2.883	2.509	2.919	3.700	0.230	1.029	0.354	1.000	1.349	5.702	3.567	0.397	0.176	1.085
S290	0.000	0.000	1.000	1.095	1.399	1.992	1.185	0.184	0.456	0.000	1.000	2.071	7.354	4.684	0.006	0.257	0.868
S289	0.000	1.000	1.000	1.556	2.332	2.902	4.347	0.124	0.577	0.707	1.000	1.444	2.981	3.874	0.216	0.138	0.838
S288	0.000	0.000	1.000	2.379	2.603	3.321	5.287	0.148	0.378	0.000	1.000	1.259	3.804	4.862	0.215	0.115	1.022
S287	0.000	0.000	1.000	0.868	1.080	1.163	1.450	0.162	0.802	0.354	1.000	0.542	1.471	1.627	0.293	0.176	0.884
S286	0.000	0.000	0.929	2.812	3.347	3.457	4.707	0.267	0.776	0.707	1.000	1.990	3.758	9.501	0.225	0.171	1.307
S285	0.000	0.000	1.000	0.985	1.235	1.394	1.855	0.134	0.487	0.000	1.000	0.680	2.177	2.247	0.261	0.153	0.911
S284	0.000	0.000	1.000	1.857	3.196	3.195	2.708	0.178	0.904	0.354	1.000	0.879	3.104	3.235	0.030	0.098	0.862
S283	0.000	0.500	1.000	1.316	4.915	2.641	4.322	0.374	0.974	1.414	1.000	1.025	3.043	2.634	0.350	0.084	0.559
S282	0.000	0.500	1.000	1.189	1.717	3.888	2.281	0.291	0.858	0.000	1.000	1.050	2.705	2.880	0.307	0.171	1.174
S281	0.000	0.000	0.964	1.695	2.629	1.810	2.928	0.107	0.450	0.000	1.000	0.987	2.396	2.958	0.253	0.087	0.618
S280	0.000	0.000	0.964	1.159	1.737	1.541	2.072	0.167	0.456	0.707	1.000	0.803	3.039	3.669	0.234	0.152	0.904
S279	0.000	0.000	0.964	2.911	2.591	2.780	3.600	0.191	0.788	0.000	1.000	2.049	3.530	4.417	0.276	0.136	0.919
S278	1.000	0.000	1.000	1.555	1.575	1.718	2.001	0.173	0.647	0.000	1.000	0.652	2.690	2.749	0.308	0.063	0.703
S277	0.000	1.000	0.893	1.290	2.730	2.494	12.004	0.063	0.392	0.000	1.000	1.122	2.537	2.783	0.296	0.079	0.609
S276	0.000	0.000	0.993	2.842	3.699	3.919	5.737	0.178	0.655	0.707	1.000	1.497	3.291	3.952	0.352	0.061	0.855
S275	0.000	0.000	1.000	2.478	3.357	4.457	4.614	0.282	0.219	0.000	1.000	2.445	6.912	5.761	0.392	0.165	0.982
S274	0.000	0.000	1.000	1.477	1.331	1.437	2.077	0.334	0.430	0.000	1.000	0.991	4.072	3.093	0.369	0.175	0.602
S273	0.000	0.500	0.964	1.992	8.126	2.835	6.016	0.159	0.788	0.354	1.000	1.339	3.579	3.129	0.211	0.165	0.696
S272	0.000	0.000	0.929	1.298	1.878	1.832	2.807	0.179	0.539	0.354	1.000	0.927	2.484	2.330	0.186	0.119	0.570
S271	0.000	0.000	0.845	1.778	2.592	3.001	3.467	0.213	0.825	0.354	1.000	2.159	5.572	4.778	0.211	0.148	1.075
S270	0.000	0.000	0.964	1.611	2.360	2.212	4.238	0.165	0.765	1.061	1.000	1.057	3.909	5.197	0.209	0.145	0.771
S269	0.000	0.500	1.000	3.522	6.198	3.841	5.593	0.137	0.983	0.707	1.000	1.716	5.683	5.229	0.375	0.108	1.066
S268	0.000	0.000	1.000	1.497	1.886	1.778	2.513	0.095	0.539	0.354	1.000	1.150	2.412	4.242	0.264	0.075	0.323
S267	0.000	0.000	1.000	1.392	1.699	1.945	2.302	0.135	0.540	0.707	1.000	1.169	3.107	3.190	0.344	0.091	0.456
S266	0.000	1.000	0.611	1.110	2.100	1.638	2.123	0.349	0.830	0.000	0.823	1.940	9.939	8.998	0.422	0.218	2.125
S265	0.000	0.000	0.964	1.500	1.872	1.898	2.526	0.202	0.816	0.000	1.000	1.087	3.168	3.286	0.437	0.211	0.858
S264	0.000	0.000	1.000	2.461	2.640	2.763	4.513	0.205	0.537	0.354	1.000	1.353	5.025	4.488	0.272	0.097	1.051
S263	0.000	0.000	1.000	1.402	1.735	2.508	4.953	0.208	0.777	0.707	1.000	0.911	3.200	3.110	0.294	0.224	1.091
S262	0.000	0.000	0.964	1.466	2.022	2.298	2.404	0.127	0.487	0.354	1.000	1.291	3.773	2.770	0.182	0.127	0.364
S261	0.000	0.500	0.761	1.692	3.659	4.919	4.466	0.144	0.717	0.354
S260	0.000	0.000	1.000	1.430	1.443	2.151	2.007	0.176	0.430	0.354	1.000	0.784	1.721	2.164	0.285	0.112	0.778
S259	0.000	0.000	1.000	1.390	1.403	1.628	2.106	0.169	0.808	0.000	1.000	0.682	2.450	2.298	0.111	0.144	0.826
S258	0.000	0.000	1.000	1.418	1.765	2.149	2.599	0.139	0.932	0.354
S257	0.000	0.000	1.000	2.908	3.005	2.264	3.581	0.216	1.002	0.354	1.000	1.280	4.774	3.626	0.309	0.175	0.843
S256	0.000	0.000	1.000	1.433	1.780	1.952	2.504	0.263	0.923	0.707	1.000	1.438	4.033	3.514	0.392	0.141	0.584
S255	0.000	0.000	0.964	0.914	1.177	1.103	1.920	0.237	0.688	0.000	1.000	0.479	1.120	1.340	0.139	0.094	0.606
S254	0.000	0.500	1.000	3.150	3.900	5.346	5.268	0.190	0.984	0.354	1.000	1.518	2.955	3.832	0.348	0.126	0.473
S253	0.000	0.000	1.000	1.886	2.400	2.671	2.995	0.093	0.309	0.354	1.000	1.449	4.371	3.832	0.204	0.114	0.822
S252	0.000	0.000	1.000	1.082	1.366	1.485	1.847	0.211	0.329	1.414	1.000	0.703	2.030	2.291	0.320	0.140	0.836
S251	0.000	1.000	0.842	2.490	9.121	10.209	8.107	0.217	0.566	0.707	0.933	1.632	6.704	4.282	0.121	0.074	0.518
S250	0.000	0.250	0.831	1.630	0.996	1.070	1.461	0.212	0.559	0.707	1.000	0.605	1.628	2.020	0.151	0.127	0.963
S249	0.000	0.000	1.000	2.662	3.282	2.877	3.917	0.256	0.309	0.354	1.000	1.396	2.949	3.383	0.211	0.042	0.671
S248	0.000	0.000	1.000	0.857	1.349	1.258	1.734	0.173	0.687	0.707	0.880	1.050	2.387	3.017	0.282	0.105	1.133
S247	0.000	0.000	1.000	0.914	1.203	1.397	1.897	0.150	0.534	0.707	1.000	0.961	2.524	2.038	0.330	0.190	0.870
S246	0.000	0.000	0.964	1.585	1.886	2.916	2.855	0.161	0.597	0.354	1.000	0.846	1.731	1.888	0.254	0.062	0.677
S245	0.000	0.000	0.999	1.112	1.376	1.830	1.806	0.173	0.467	0.354	1.000	0.869	2.388	2.087	0.237	0.171	0.791
S244	0.000	0.000	1.000	0.953	1.281	1.560	1.686	0.131	0.728	0.000	1.000	0.673	2.292	2.646	0.282	0.126	0.747
S243	0.000	1.000	1.000	7.331	5.069	4.736	5.369	0.143	0.528	0.000	1.000	3.533	6.994	6.389	0.214	0.094	0.572
S242	0.000	0.000	1.000	1.201	1.539	1.483	2.115	0.264	0.802	1.061	1.000	0.762	2.287	1.864	0.340	0.331	1.005
S241	0.000	0.000	0.893	1.175	2.083	1.585	1.329	0.122	0.467	0.354	1.000	1.059	2.071	2.620	0.040	0.154	0.933
S240	0.000	0.500	1.000	2.894	3.176	3.971	4.478	0.142	0.705	1.061	1.000	3.196	6.182	7.181	0.250	0.129	0.658
S239	0.000	0.000	1.000	2.289	2.245	2.572	2.499	0.210	0.650	0.000	1.000	1.005	2.917	3.616	0.322	0.118	0.768
S238	0.000	0.000	1.000	2.070	2.288	2.816	3.668	0.296	0.676	0.000	1.000	1.546	4.031	6.543	0.296	0.234	0.941
S237	0.000	0.000	1.000	1.361	2.993	1.490	2.610	0.137	0.955	0.354	1.000	1.014	3.942	2.803	0.148	0.202	0.644
S236	0.000	0.000	0.960	0.934	1.143	1.283	1.829	0.240	0.519	0.000	1.000	0.797	1.704	2.118	0.313	0.066	0.711
S235	0.000	0.000	1.000	2.383	2.271	2.403	2.334	0.172	0.838	0.354	1.000	1.143	2.452	2.218	0.263	0.184	0.930
S234	0.000	0.000	1.000	1.212	1.691	1.982	2.604	0.210	0.849	0.000	0.993	0.883	2.296	2.976	0.282	0.181	1.221
S233	0.000	0.000	0.929	1.401	2.989	3.622	2.917	0.089	0.784	0.000	1.000	1.938	3.655	4.220	0.394	0.130	0.746
S232	0.000	0.000	1.000	1.761	3.785	3.646	3.405	0.205	0.525	0.000	1.000	1.278	12.125	3.208	0.330	0.170	0.973
S231	0.000	0.000	1.000	1.645	2.005	2.036	2.196	0.200	0.926	0.707	1.000	0.887	2.679	2.399	0.248	0.181	0.958
S230	0.000	0.000	1.000	1.914	1.967	2.041	2.239	0.153	0.418	0.000	1.000	1.268	2.846	3.788	0.288	0.119	0.947
S229	1.000	0.000	1.000	1.723	1.708	2.732	2.950	0.131	0.779	0.354	1.000	0.989	2.849	4.752	0.242	0.164	0.810
S228	0.000	0.000	1.000	1.488	1.607	1.963	2.861	0.193	0.863	0.354	1.000	1.331	7.153	7.466	0.333	0.123	0.703
S227	0.000	0.000	0.964	1.066	1.827	1.870	1.657	0.187	0.629	0.707	0.997	0.733	3.168	2.736	0.210	0.119	0.727
S226	2.000	0.000	1.000	3.319	2.895	3.100	3.128	0.160	0.643	1.061	1.000	6.592	5.428	5.425	0.237	0.120	1.068
S225	0.000	0.000	1.000	1.641	3.665	2.710	2.831	0.128	0.612	0.000	1.000	1.420	3.716	3.218	0.415	0.168	0.681
S224	1.000	1.000	0.997	1.217	4.576	1.838	2.326	0.154	0.565	0.354	1.000	0.841	2.700	2.335	0.211	0.094	0.413
S223	0.000	0.500	0.929	1.219	1.775	3.688	2.220	0.356	0.934	1.061	1.000	1.143	2.937	2.432	0.213	0.214	0.947
S222	0.000	0.500	0.979	1.162	5.108	1.766	1.887	0.198	0.719	0.354	1.000	2.363	3.380	2.489	0.248	0.136	0.822
S221	0.000	0.000	1.000	1.763	3.545	2.856	3.950	0.152	0.834	0.354	1.000	1.160	3.761	3.475	0.000	0.221	0.779
S220	0.000	0.000	1.000	1.259	1.672	1.834	3.072	0.261	0.558	0.354	1.000	0.809	2.245	2.584	0.093	0.185	0.985
S219	0.000	0.000	1.000	2.332	2.565	2.827	3.985	0.157	0.661	0.000	1.000	1.864	3.553	3.753	0.247	0.114	1.028
S218	0.000	0.000	1.000	1.692	2.157	1.918	3.558	0.313	0.646	0.000	1.000	1.376	1.845	3.248	0.272	0.038	0.821
S217	0.000	0.000	0.832	1.126	2.446	1.516	2.970	0.129	0.655	0.707	0.943	0.856	2.071	2.385	0.193	0.131	0.750
S216	0.000	0.000	0.863	1.110	1.271	1.821	1.819	0.139	0.519	0.707	1.000	0.705	1.403	1.798	0.212	0.122	0.583
S215	0.000	0.000	0.964	1.078	2.644	2.500	3.646	0.177	0.585	1.414	1.000	2.047	4.143	5.216	0.263	0.185	0.642
S214	0.000	0.000	1.000	2.363	1.928	2.262	3.313	0.188	0.724	0.354
S213	0.000	0.000	1.000	1.363	1.957	1.964	2.115	0.188	0.487	0.000	1.000	0.767	2.549	2.478	0.375	0.145	0.874
S212	0.000	0.000	1.000	1.946	2.575	2.921	3.251	0.288	0.825	1.061	1.000	1.648	3.851	3.959	0.366	0.210	0.778
S211	0.000	0.000	1.000	1.293	1.909	1.711	3.389	0.201	0.626	0.354	1.000	1.158	3.007	1.349	0.258	0.169	0.744
S210	0.000	0.500	1.000	1.487	5.489	1.694	2.440	0.142	0.643	0.707
S209	0.000	0.000	1.000	1.174	1.598	1.537	1.818	0.144	0.407	0.707	0.997	1.275	2.427	2.794	0.359	0.076	0.995
S208	0.000	0.000	1.000	1.826	2.777	1.934	3.004	0.181	0.967	0.707	1.000	0.950	3.373	3.362	0.309	0.172	0.583
S207	0.000	0.000	0.964	0.923	2.363	1.500	2.005	0.115	0.713	0.354	1.000	0.644	1.832	1.849	0.290	0.134	0.794
S206	0.000	0.000	1.000	2.218	2.882	2.923	3.004	0.154	0.549	0.354	1.000	1.833	4.121	4.326	0.357	0.127	0.683
S205	0.000	0.000	1.000	1.691	3.114	2.646	2.991	0.096	0.741	0.707	1.000	1.818	3.862	4.148	0.236	0.127	0.756
S204	0.000	0.000	1.000	1.502	2.257	2.018	2.572	0.204	1.073	0.707	1.000	1.138	2.716	3.237	0.298	0.213	1.507
S203	0.000	0.500	0.900	2.347	1.547	4.431	4.086	0.128	0.693	0.354	1.000	1.455	24.927	8.559	0.120	0.128	0.739
S202	0.000	0.000	1.000	1.514	1.530	3.165	2.867	0.114	0.000	0.000	1.000	1.150	5.148	5.876	0.073	0.192	0.615
S201	0.000	0.000	1.000	1.520	2.244	2.145	2.828	0.240	0.779	0.000	1.000	1.546	4.219	4.757	0.206	0.134	0.806
S200	0.000	0.000	0.893	1.161	2.862	2.185	1.961	0.205	0.863	0.000	1.000	0.597	2.273	3.959	0.148	0.187	1.214
S199	0.000	0.500	0.857	1.475	1.957	2.210	2.558	0.245	0.737	0.354	1.000	2.255	7.234	4.897	0.390	0.112	0.925
S198	0.000	0.500	1.000	3.866	1.979	1.958	2.454	0.269	0.537	0.354	1.000	0.794	2.530	2.470	0.316	0.199	1.773
S197	0.000	0.000	1.000	2.106	2.034	2.028	3.471	0.315	0.615	0.354	1.000	1.272	3.374	3.045	0.404	0.189	1.099
S196	0.000	0.000	1.000	1.047	2.106	1.463	1.922	0.204	0.528	0.707	1.000	1.577	1.846	3.665	0.124	0.097	0.822
S195	0.000	0.000	1.000	1.344	1.335	1.407	1.988	0.130	0.199	0.000	1.000	0.698	2.362	2.318	0.192	0.067	0.565
S194	0.000	0.000	1.000	1.344	1.777	2.121	3.638	0.295	0.525	0.354	1.000	0.892	3.057	2.897	0.375	0.172	1.364
S193	0.000	0.000	1.000	1.170	1.814	1.873	2.174	0.168	0.724	0.707	1.000	0.988	1.840	2.742	0.091	0.072	0.605
S192	0.000	0.000	1.000	1.372	1.715	1.964	2.305	0.183	0.802	0.707	1.000	1.116	2.321	2.656	0.303	0.098	0.360
S191	0.000	0.000	1.000	1.085	1.395	1.595	3.164	0.145	0.704	1.061	1.000	0.804	2.291	2.545	0.376	0.148	0.700
S190	0.000	0.000	0.913	1.328	1.929	2.072	2.971	0.169	0.525	0.707
S189	0.000	1.000	0.595	1.268	4.890	1.909	2.418	0.229	0.828	0.000	1.000	1.913	5.913	4.755	0.485	0.084	0.554
S188	0.000	0.000	0.929	2.201	2.724	2.608	4.604	0.202	0.668	0.354	1.000	1.701	5.154	5.296	0.231	0.110	0.621
S187	0.000	0.000	0.917	2.538	4.190	3.347	4.068	0.406	0.973	0.354	1.000	1.534	3.988	4.139	0.442	0.208	1.557
S186	0.000	0.500	1.000	2.081	2.853	2.921	3.352	0.136	0.916	0.354	1.000	1.349	3.836	4.424	0.483	0.172	0.796
S185	0.000	0.000	1.000	0.853	1.105	1.626	1.819	0.170	0.657	0.707	1.000	0.844	2.348	2.915	0.273	0.179	0.529
S184	0.000	0.500	0.843	2.114	5.858	7.475	5.366	0.242	0.617	0.354	0.787	2.367	3.413	10.973	0.255	0.083	0.766
S183	0.000	0.000	1.000	1.898	3.311	2.808	4.702	0.211	0.814	0.000	1.000	6.765	3.728	4.513	0.212	0.175	0.884
S182	0.000	0.000	1.000	2.719	3.090	3.467	4.974	0.156	0.834	0.707	1.000	0.914	3.753	3.432	0.330	0.167	0.586
S181	0.000	0.000	0.964	1.938	3.761	3.473	4.387	0.224	0.797	0.000	0.827	2.174	2.640	4.642	0.170	0.064	0.511
S180	0.000	0.000	0.950	1.185	2.342	1.426	2.299	0.064	0.462	0.354	1.000	0.942	1.927	2.662	0.090	0.096	0.494
S179	0.000	0.250	1.000	2.990	2.102	2.797	4.539	0.154	0.358	0.707	1.000	1.275	3.775	3.626	0.273	0.111	0.491
S178	0.000	0.000	1.000	1.321	1.784	1.567	2.550	0.194	0.834	0.354	1.000	0.922	2.140	2.376	0.186	0.087	1.113
S177	0.000	0.500	0.926	1.667	3.152	5.510	3.462	0.232	0.629	0.000	1.000	1.874	3.982	6.979	0.409	0.095	1.507
S176	0.000	0.000	0.964	2.410	3.243	3.747	4.315	0.272	0.796	0.000	1.000	1.618	4.845	4.721	0.266	0.165	0.815
S175	0.000	0.500	0.893	2.497	2.310	3.097	6.320	0.202	0.739	1.061	1.000	1.042	2.680	2.551	0.228	0.195	1.627
S174	0.000	0.500	0.964	1.572	2.908	2.124	7.362	0.083	0.663	0.707	1.000	0.745	3.976	8.976	0.100	0.060	0.929
S173	0.000	0.000	1.000	1.561	2.609	2.147	3.123	0.246	0.724	0.000	1.000	1.380	4.853	4.855	0.340	0.169	0.812
S172	0.000	0.000	0.857	1.778	2.397	2.265	2.895	0.318	0.859	1.061	1.000	1.530	4.295	4.024	0.451	0.226	0.739
S171	0.000	0.000	0.929	1.335	2.256	2.601	2.723	0.149	0.487	0.354	1.000	0.801	7.868	2.918	0.258	0.129	0.673
S170	0.000	0.500	0.905	2.818	6.842	5.090	5.707	0.293	1.037	0.707	1.000	4.561	13.513	12.296	0.432	0.129	1.262
S169	0.000	0.000	1.000	2.534	2.809	3.751	5.201	0.236	0.854	0.707
S168	0.000	0.000	1.000	1.916	2.226	1.748	2.163	0.184	0.545	0.000	1.000	1.154	2.518	3.340	0.089	0.150	0.824
S167	0.000	0.000	1.000	0.813	1.325	1.300	2.202	0.249	1.127	0.707
S166	0.000	0.000	1.000	0.926	1.113	1.126	1.459	0.162	0.635	0.000	1.000	0.859	2.224	1.914	0.247	0.114	0.318
S165	0.000	0.000	1.000	1.699	2.933	2.793	3.059	0.196	0.908	0.707	1.000	1.470	3.302	3.869	0.153	0.142	0.822
S164	0.000	0.000	1.000	1.006	1.283	1.474	1.791	0.124	0.467	0.000	1.000	0.571	2.970	2.437	0.118	0.149	0.664
S163	0.000	0.500	1.000	1.618	2.576	6.214	3.047	0.122	0.719	0.354	1.000	1.189	2.977	2.833	0.251	0.124	1.163
S162	0.000	0.000	0.973	2.022	2.397	2.974	3.504	0.132	0.557	0.000	1.000	1.221	4.145	3.221	0.112	0.118	0.561
S161	0.000	0.250	1.000	2.168	3.121	3.229	5.050	0.301	0.961	0.354	1.000	1.816	3.526	4.034	0.449	0.125	1.208
S160	0.000	0.000	1.000	1.608	3.534	1.646	2.242	0.154	0.692	0.000	1.000	0.929	3.023	2.503	0.290	0.158	0.890
S159	0.000	0.000	0.964	0.970	1.526	1.697	2.031	0.144	0.687	0.000	1.000	0.721	2.179	2.460	0.106	0.080	0.543
S158	0.000	0.000	1.000	1.553	1.578	1.817	2.292	0.129	0.366	0.000	0.987	0.906	5.601	2.647	0.323	0.091	0.707
S157	0.000	0.000	1.000	1.395	2.264	2.050	3.377	0.137	0.585	0.354	1.000	0.722	1.993	2.006	0.352	0.178	0.979
S156	0.000	0.000	1.000	1.118	1.418	1.802	1.844	0.207	0.737	0.354	1.000	0.742	1.172	1.663	0.198	0.182	0.685
S155	0.000	0.250	1.000	1.599	2.294	2.304	3.285	0.329	0.671	1.768	1.000	1.568	4.559	4.944	0.449	0.110	1.097
S154	0.000	0.000	1.000	0.979	1.271	1.328	1.867	0.185	0.637	0.354	1.000	0.780	1.867	2.369	0.203	0.169	0.534
S153	0.500	0.250	0.964	1.959	1.466	1.382	2.978	0.160	1.068	1.061	1.000	1.481	2.874	3.135	0.127	0.162	0.842
S152	0.000	0.500	1.000	1.255	2.559	8.312	2.170	0.349	0.903	0.707	1.000	0.956	5.665	6.348	0.304	0.210	1.529
S151	0.000	1.000	0.787	6.055	1.806	7.617	1.062	0.199	0.655	1.414	0.933	3.456	4.432	4.996	0.221	0.141	1.112
S150	0.000	0.000	0.929	1.602	1.898	2.199	2.757	0.247	0.758	0.354	1.000	1.009	3.496	2.574	0.408	0.125	1.258
S149	0.000	0.000	1.000	1.038	1.291	1.402	1.797	0.117	0.724	0.354	1.000	0.831	2.742	2.254	0.252	0.083	0.464
S148	0.000	1.000	0.804	3.082	1.542	11.678	5.329	0.167	1.056	0.354	1.000	0.808	1.693	1.999	0.281	0.176	0.830
S147	0.000	0.000	1.000	2.631	2.598	3.734	4.462	0.093	0.644	0.354
S146	0.000	0.250	1.000	0.877	1.449	1.356	1.819	0.157	1.108	0.707
S145	0.000	0.000	1.000	1.724	2.164	2.095	3.415	0.188	0.785	0.707	1.000	1.116	2.437	3.312	0.087	0.200	0.874
S144	0.000	0.000	1.000	1.564	3.449	2.245	2.949	0.303	0.561	0.354
S143	0.000	0.000	0.964	1.230	1.597	1.831	2.758	0.176	0.638	0.000
S142	0.000	0.000	1.000	1.166	1.522	1.615	2.256	0.265	0.888	0.354	1.000	0.843	4.184	2.552	0.300	0.136	0.967
S141	0.000	0.000	0.929	1.679	3.655	2.994	3.882	0.223	0.979	1.414	1.000	1.545	3.443	3.624	0.306	0.127	1.087
S140	0.000	0.000	1.000	2.317	2.107	2.418	2.455	0.203	0.585	0.707
S139	0.000	0.000	0.857	2.020	2.878	2.427	3.706	0.252	0.823	1.061
S138	0.000	0.000	1.000	1.607	2.167	2.391	3.089	0.099	0.450	0.000	1.000	1.124	2.885	3.328	0.177	0.049	0.450
S137	0.000	0.000	1.000	2.415	3.982	3.379	4.709	0.201	0.802	0.707	1.000	2.073	3.441	8.485	0.330	0.167	1.170
S136	0.000	0.000	1.000	1.253	1.406	1.455	1.588	0.212	0.687	0.354	1.000	1.081	2.923	2.740	0.279	0.113	0.689
S135	0.000	0.000	1.000	1.269	1.598	1.561	1.911	0.147	0.626	1.061	1.000	0.939	1.946	2.272	0.103	0.082	0.667
S134	0.000	0.000	1.000	1.375	1.852	1.804	2.320	0.185	0.656	0.000	1.000	1.236	3.236	3.321	0.293	0.167	1.203
S133	0.000	0.000	1.000	1.079	1.699	1.365	1.155	0.164	0.398	0.354
S132	0.000	0.000	0.952	1.508	1.876	2.020	2.664	0.143	0.667	0.354	1.000	1.270	2.888	3.575	0.352	0.153	0.627
S131	0.000	0.000	0.964	1.548	3.060	2.143	3.262	0.174	0.765	0.707	1.000	1.533	3.425	3.826	0.298	0.096	0.449
S130	0.000	0.000	0.951	1.389	1.623	2.126	3.081	0.246	0.664	0.354	0.620	0.676	18.939	2.733	0.248	0.111	1.191
S129	0.000	0.000	1.000	2.034	1.930	2.243	3.098	0.235	0.727	0.707	0.780	1.212	3.612	3.814	0.384	0.165	0.851
S128	0.000	0.000	0.944	1.295	1.707	2.099	2.268	0.071	0.724	0.000	1.000	1.130	8.028	6.902	0.240	0.139	0.913
S127	0.000	1.000	0.750	1.872	14.156	2.988	3.551	0.260	0.768	0.354	1.000	1.523	5.212	4.341	0.425	0.088	0.719
S126	0.000	0.000	0.999	0.958	1.471	2.260	3.916	0.161	0.557	1.061	1.000	0.828	2.344	2.773	0.261	0.139	0.815
S125	0.000	0.000	1.000	0.904	1.103	0.945	1.365	0.122	0.629	0.707	1.000	0.805	1.376	1.623	0.135	0.086	0.324
S124	0.000	0.000	1.000	1.127	1.696	1.590	3.555	0.212	0.803	0.707	1.000	0.944	2.528	4.157	0.057	0.052	0.821
S123	1.000	0.000	0.964	1.012	1.659	2.681	2.070	0.189	0.467	0.707	1.000	0.659	1.241	0.925	0.182	0.126	1.112
S122	0.000	0.500	0.964	2.966	3.771	4.138	2.754	0.216	0.981	0.354	1.000	0.985	3.663	2.929	0.210	0.255	0.903
S121	0.000	0.500	1.000	1.091	2.171	3.481	2.519	0.101	0.571	0.354	1.000	1.311	2.977	4.333	0.174	0.080	0.664
S120	0.000	0.250	1.000	2.460	4.160	2.681	2.985	0.194	0.528	0.000	1.000	1.511	3.571	3.440	0.331	0.096	0.761
S119	0.000	0.500	0.964	3.599	3.987	5.267	5.465	0.122	0.447	0.000	0.933	2.276	17.802	8.090	0.268	0.132	0.749
S118	0.000	0.000	1.000	1.461	2.936	2.002	2.378	0.156	0.539	0.000
S117	0.000	0.250	0.893	2.431	1.215	1.241	2.588	0.253	0.864	0.707	1.000	1.149	1.819	2.164	0.403	0.227	0.856
S116	0.000	0.000	0.964	1.238	1.922	2.067	2.462	0.207	0.687	0.354	1.000	0.607	1.628	1.950	0.075	0.148	0.811
S115	0.000	0.000	1.000	1.634	1.648	1.923	2.453	0.135	0.728	0.000	1.000	1.029	2.274	2.743	0.287	0.135	0.859
S114	0.000	0.000	0.977	0.964	1.467	1.984	2.204	0.137	0.742	1.061	1.000	1.120	2.525	2.515	0.289	0.145	0.800
S113	0.000	0.500	1.000	2.235	2.564	4.278	3.450	0.164	0.692	0.000	1.000	0.731	2.530	2.292	0.105	0.106	0.636
S112	0.000	0.000	1.000	1.542	1.687	1.797	2.863	0.239	0.816	0.354	1.000	0.604	7.180	3.415	0.196	0.193	1.186
S111	0.000	0.000	1.000	1.346	2.048	1.928	2.283	0.225	0.908	0.000	1.000	1.443	3.507	4.301	0.384	0.200	0.542
S110	0.000	1.000	0.444	0.500	1.035	0.625	1.052	0.216	0.956	0.707	0.430	0.346	0.783	0.567	0.295	0.278	0.713
S109	0.000	0.000	0.929	1.942	2.720	2.470	3.267	0.212	0.984	0.707	1.000	1.137	3.840	3.609	0.312	0.116	1.376
S108	0.000	0.000	1.000	1.043	1.789	1.661	2.620	0.200	0.687	0.707	1.000	0.712	1.403	1.920	0.301	0.080	0.766
S107	0.000	0.250	1.000	1.123	1.424	1.703	2.584	0.277	0.814	0.707	1.000	0.644	1.363	1.959	0.481	0.310	1.141
S106	0.000	0.000	1.000	1.551	1.918	2.582	3.230	0.263	0.920	0.000
S105	0.000	0.500	1.000	1.146	1.703	3.678	2.709	0.101	0.838	0.707	1.000	0.816	2.487	2.952	0.252	0.333	0.641
S104	0.000	0.000	0.874	1.208	2.154	1.813	1.564	0.169	0.727	0.707	1.000	0.878	1.450	1.424	0.345	0.158	0.903
S103	0.000	0.000	1.000	1.111	2.064	1.916	2.653	0.077	0.557	0.000	1.000	1.232	2.563	3.069	0.217	0.203	1.055
S102	0.000	0.000	0.970	1.148	2.116	1.962	2.562	0.166	0.819	0.707	1.000	1.125	2.395	3.074	0.311	0.096	0.549
S101	0.000	0.000	1.000	1.567	1.772	2.167	2.586	0.189	0.309	0.354	1.000	1.095	3.537	3.494	0.239	0.069	0.367
S100	0.000	0.000	1.000	1.696	2.091	1.793	2.910	0.141	0.577	0.000	1.000	0.871	2.295	3.007	0.288	0.100	0.576
S099	0.000	0.750	0.886	2.410	2.758	2.462	5.870	0.391	0.724	0.000	1.000	0.837	2.627	2.711	0.308	0.127	1.064
S098	0.000	0.000	1.000	1.644	1.681	1.968	3.404	0.207	0.957	0.707
S097	0.000	0.000	1.000	1.009	1.301	1.626	1.790	0.118	0.410	0.354	1.000	0.570	1.719	2.007	0.220	0.063	0.843
S096	0.000	0.000	1.000	1.673	1.483	1.753	2.445	0.350	0.924	1.414	1.000	2.442	2.810	4.786	0.444	0.288	1.275
S095	0.000	0.000	1.000	1.345	1.592	1.836	1.822	0.150	0.774	0.707	1.000	1.280	2.318	2.397	0.269	0.218	0.748
S094	0.000	0.000	0.964	1.700	2.307	2.572	4.046	0.185	0.855	0.000	0.937	0.940	2.970	2.941	0.264	0.115	1.100
S093	0.000	0.000	1.000	1.897	2.166	2.882	3.538	0.135	0.724	0.000	1.000	2.257	4.398	4.882	0.243	0.140	0.887
S092	0.000	0.000	1.000	1.178	2.504	2.125	2.874	0.200	0.814	0.354	1.000	0.708	2.452	2.363	0.212	0.157	0.717
S091	0.000	0.000	0.893	0.852	1.412	1.357	2.734	0.090	0.707	0.707
S090	0.000	0.000	1.000	1.984	2.113	2.294	3.323	0.246	0.618	0.354	1.000	1.811	6.279	3.608	0.350	0.169	1.019
S089	0.000	1.000	0.891	6.854	2.193	2.354	2.849	0.201	0.545	0.354	1.000	0.582	1.907	2.787	0.401	0.211	0.667
S088	0.000	0.000	1.000	2.068	2.503	3.000	4.065	0.237	0.410	0.000	1.000	1.700	8.566	5.096	0.233	0.078	0.789
S087	0.000	0.250	1.000	1.566	4.313	2.873	2.848	0.196	0.652	0.707	1.000	1.078	3.060	3.713	0.295	0.151	1.098
S086	0.000	0.000	0.924	2.013	2.597	2.479	3.682	0.292	0.834	0.000	1.000	1.401	3.548	3.974	0.351	0.142	1.122
S085	0.000	0.000	0.880	1.307	3.385	2.514	2.843	0.164	0.756	0.354	1.000	1.190	3.108	3.228	0.114	0.117	0.914
S084	0.000	0.000	1.000	1.795	1.887	2.720	3.042	0.206	0.797	0.354	1.000	2.035	3.961	3.637	0.293	0.179	0.954
S083	0.000	0.500	1.000	2.247	4.753	4.815	5.163	0.214	0.110	0.354	1.000	2.153	5.106	5.242	0.078	0.141	0.602
S082	0.000	0.000	0.999	0.789	1.058	1.190	1.354	0.291	1.384	0.354
S081	0.000	0.000	0.964	1.177	2.558	2.437	2.485	0.166	0.825	0.707	1.000	0.732	1.635	1.705	0.184	0.136	0.928
S080	0.000	0.500	1.000	1.130	3.723	2.102	3.188	0.426	0.817	1.061	1.000	0.892	4.189	3.243	0.422	0.237	1.043
S079	0.000	0.500	1.000	1.320	2.051	2.116	4.948	0.253	0.836	0.000	1.000	0.927	7.657	3.346	0.451	0.207	0.919
S078	0.000	0.000	1.000	1.120	1.865	1.645	2.061	0.170	0.418	0.354	0.877	1.845	1.939	2.375	0.194	0.112	0.753
S077	0.000	0.000	1.000	1.327	1.885	1.707	2.353	0.360	0.862	0.354	1.000	1.715	2.902	3.267	0.428	0.341	0.892
S076	0.000	0.000	0.964	1.597	2.249	2.194	2.502	0.153	0.675	0.707	1.000	1.465	4.299	4.989	0.205	0.131	0.716
S075	0.000	0.000	1.000	1.848	3.184	3.326	4.551	0.218	0.858	0.354	1.000	2.111	5.419	4.384	0.282	0.188	1.006
S074	0.000	0.000	0.929	1.309	1.640	1.664	1.860	0.186	0.713	0.707	1.000	0.766	2.207	2.166	0.281	0.087	0.563
S073	0.000	0.000	0.964	1.626	1.868	1.930	2.479	0.190	0.666	0.000	1.000	1.467	2.838	2.796	0.148	0.098	0.619
S072	0.000	1.000	1.000	1.283	14.469	2.321	2.315	0.175	0.559	0.707	1.000	0.391	1.086	1.206	0.138	0.142	1.026
S071	0.000	0.000	1.000	1.652	2.506	2.479	2.670	0.185	0.834	0.354	1.000	1.047	2.745	3.909	0.337	0.136	0.742
S070	0.000	0.250	0.788	1.907	2.070	2.267	2.458	0.169	0.643	0.000
S069	0.000	0.500	0.952	3.627	1.232	1.318	6.019	0.192	0.509	0.707
S068	0.000	0.750	0.621	1.335	1.479	2.720	3.725	0.163	0.320	0.354	0.710	0.829	2.248	6.026	0.038	0.103	0.480
S067	0.000	0.000	0.910	1.005	1.141	1.345	1.457	0.186	0.545	0.707	0.933	0.762	1.910	1.680	0.294	0.067	0.821
S066	0.000	1.000	1.000	2.697	3.905	6.625	5.193	0.437	0.868	1.061	1.000	1.769	14.795	5.871	0.307	0.156	1.491
S065	0.000	0.000	0.964	1.282	1.868	2.154	2.847	0.226	0.675	0.000	1.000	1.313	2.621	2.959	0.216	0.082	0.524
S064	0.000	0.000	1.000	2.103	3.373	2.784	4.023	0.079	0.823	0.354	1.000	1.440	4.617	3.835	0.264	0.103	0.635
S063	0.000	0.000	0.891	1.521	1.713	2.049	2.047	0.188	0.676	0.354	1.000	0.980	3.679	3.757	0.131	0.160	0.579
S062	0.000	0.000	1.000	1.922	2.015	2.030	2.409	0.084	0.534	0.354	1.000	0.898	2.195	2.952	0.268	0.044	0.767
S061	0.000	0.500	1.000	0.817	6.877	1.362	1.873	0.145	0.509	0.000	1.000	0.715	2.218	2.420	0.111	0.143	0.647
S060	0.000	0.000	0.964	1.567	2.094	2.395	2.996	0.210	0.509	0.354	1.000	1.387	3.175	3.327	0.328	0.163	0.717
S059	0.000	0.000	0.997	2.304	2.234	2.914	2.421	0.255	0.907	0.707	1.000	0.984	3.476	3.533	0.399	0.280	1.177
S058	0.000	0.000	1.000	2.149	3.804	2.734	4.261	0.209	0.647	1.414
S057	0.000	0.000	1.000	3.019	2.526	2.324	3.804	0.167	0.724	0.354	1.000	1.831	3.418	3.913	0.279	0.182	1.018
S056	0.000	0.000	1.000	1.477	1.661	2.534	2.419	0.181	0.577	1.061	1.000	0.946	2.654	2.670	0.345	0.224	1.260
S055	0.000	0.000	0.964	1.084	1.428	1.200	1.911	0.237	0.715	0.000	1.000	0.773	2.163	1.956	0.394	0.166	0.490
S054	0.000	0.000	0.857	2.392	3.006	2.951	3.400	0.158	0.597	0.707	1.000	2.146	5.656	5.348	0.285	0.205	0.614
S053	0.000	0.000	1.000	1.324	2.183	1.636	2.435	0.117	0.571	0.000	1.000	0.720	2.328	2.217	0.240	0.112	0.463
S052	0.000	0.000	1.000	1.873	2.227	2.108	2.541	0.140	0.537	0.354	1.000	1.659	2.329	2.428	0.201	0.100	0.722
S051	0.000	0.000	1.000	2.072	2.736	2.599	3.642	0.157	0.199	0.354	1.000	2.641	7.243	6.177	0.233	0.083	0.833
S050	0.000	0.000	1.000	1.454	2.037	2.662	2.679	0.216	0.460	0.000	1.000	0.875	3.154	3.247	0.158	0.165	0.967
S049	0.000	0.500	0.929	2.178	4.859	3.562	5.110	0.207	0.528	0.000	1.000	2.354	5.526	6.149	0.393	0.169	0.792
S048	0.000	0.000	1.000	1.377	1.914	2.253	2.993	0.196	0.537	0.354	1.000	0.885	2.191	2.906	0.174	0.158	1.156
S047	0.000	1.000	0.779	4.321	1.188	0.905	3.752	0.184	0.089	0.000
S046	0.000	0.000	1.000	1.099	1.136	1.422	1.599	0.100	0.666	0.707	1.000	0.674	1.779	2.162	0.282	0.160	0.898
S045	0.000	0.000	0.893	2.371	2.143	2.942	3.046	0.258	0.606	0.354	1.000	1.308	4.389	4.066	0.306	0.120	0.915
S044	0.000	0.000	0.964	1.625	2.098	2.295	3.221	0.170	0.699	0.354	1.000	2.691	4.600	5.542	0.436	0.192	0.892
S043	0.000	0.000	0.964	1.304	1.868	1.949	2.288	0.219	0.398	0.354	1.000	1.117	2.376	2.369	0.211	0.197	0.773
S042	0.000	0.000	1.000	1.204	1.659	1.917	2.408	0.160	0.545	1.061	1.000	1.374	2.951	3.249	0.243	0.103	0.441
S041	0.000	0.000	1.000	1.455	1.949	1.693	2.056	0.181	0.732	0.354	1.000	1.160	2.657	2.257	0.361	0.106	0.660
S040	0.000	0.000	1.000	1.422	1.965	1.220	1.954	0.121	0.687	0.707	0.933	1.805	3.320	2.609	0.305	0.155	0.438
S039	0.000	0.000	1.000	2.092	3.134	2.945	2.904	0.190	0.656	0.707	1.000	0.937	2.700	2.954	0.281	0.124	0.591
S038	0.000	0.500	0.964	3.351	6.988	3.231	6.149	0.196	0.687	0.354	1.000	4.282	7.684	6.381	0.314	0.186	1.244
S037	0.000	0.500	1.000	0.770	4.167	2.955	1.453	0.104	0.398	0.354	1.000	0.727	1.784	2.165	0.331	0.132	0.575
S036	0.000	0.000	0.996	1.325	1.921	1.766	2.582	0.226	0.728	0.354	1.000	1.336	3.207	2.723	0.288	0.116	0.972
S035	0.000	0.000	0.993	2.358	2.258	2.601	2.810	0.182	0.837	0.354	1.000	1.342	4.265	4.077	0.329	0.148	0.424
S034	0.000	0.000	1.000	2.966	3.338	4.372	5.711	0.182	0.617	0.354	1.000	2.073	5.942	5.833	0.020	0.139	0.685
S033	0.000	0.000	1.000	0.692	0.890	1.091	1.226	0.088	0.288	0.000	1.000	0.436	1.694	1.753	0.060	0.064	0.339
S032	0.000	0.000	1.000	1.131	1.565	2.121	2.215	0.148	0.736	0.000	1.000	0.714	2.044	2.116	0.173	0.148	0.735
S031	0.000	0.000	0.964	1.509	1.611	1.729	1.971	0.224	0.830	0.354	1.000	0.744	2.250	3.086	0.337	0.097	0.834
S030	0.000	0.500	0.776	3.296	2.679	4.596	4.462	0.222	0.948	0.000	0.930	1.107	2.667	3.357	0.242	0.070	0.817
S029	0.000	0.500	0.913	1.302	6.534	3.338	3.826	0.167	1.146	0.000
S028	0.000	0.000	0.964	1.741	3.268	2.695	3.601	0.172	0.585	0.707	1.000	1.445	4.530	3.751	0.239	0.118	0.828
S027	0.000	0.000	1.000	1.369	1.777	2.340	4.058	0.161	0.675	0.000	1.000	0.975	3.185	3.115	0.120	0.121	0.696
S026	0.000	0.500	1.000	4.696	5.658	5.382	6.233	0.173	0.790	0.354	1.000	2.717	9.315	7.220	0.223	0.093	0.695
S025	0.000	0.500	1.000	2.561	2.680	5.594	4.803	0.317	0.748	0.000	1.000	2.586	7.020	6.428	0.311	0.133	0.724
S024	0.000	0.000	1.000	2.115	3.116	3.465	3.902	0.182	0.835	1.414	1.000	1.579	3.468	3.279	0.223	0.171	0.831
S023	0.000	0.000	1.000	1.548	1.875	2.191	2.886	0.277	0.674	0.000
S022	0.000	0.000	1.000	1.121	1.435	1.802	2.783	0.217	0.610	0.000	1.000	0.927	3.102	2.641	0.460	0.272	1.053
S021	0.000	0.750	1.000	4.232	3.404	6.555	7.422	0.484	0.819	1.061	1.000	1.937	10.164	5.709	0.407	0.175	1.159
S020	0.000	0.000	0.860	1.631	2.575	2.375	3.247	0.256	1.017	1.414	1.000	1.131	2.173	2.581	0.246	0.123	0.690
S019	0.000	0.000	0.893	1.980	2.789	3.059	5.708	0.205	0.744	0.354	1.000	1.819	3.945	3.614	0.237	0.116	0.750
S018	0.000	0.000	1.000	1.658	2.039	2.027	2.613	0.146	0.656	1.061	1.000	1.693	4.195	6.711	0.076	0.088	0.957
S017	0.000	0.000	0.964	1.299	1.681	1.911	2.055	0.201	0.937	0.354	1.000	0.805	2.606	7.445	0.169	0.140	0.798
S016	0.000	0.500	0.964	4.064	5.145	3.684	5.762	0.076	0.551	0.354	1.000	2.738	5.341	4.064	0.186	0.072	0.345
S015	0.000	0.000	1.000	1.045	1.275	1.418	2.595	0.292	0.713	0.354	1.000	0.856	2.214	2.010	0.315	0.301	0.895
S014	0.000	0.000	1.000	0.818	1.350	1.324	1.521	0.225	0.467	0.354
S013	0.000	0.000	1.000	1.071	1.270	1.418	2.425	0.093	0.710	0.000	1.000	0.787	2.355	2.585	0.416	0.093	0.514
S012	0.000	0.000	0.917	3.183	5.186	5.265	5.370	0.193	0.577	0.354	0.990	1.808	6.248	6.098	0.018	0.129	1.140
S011	0.000	0.500	1.000	1.871	2.802	2.380	3.382	0.155	0.814	0.354	1.000	1.188	3.283	3.533	0.313	0.130	0.769
S010	0.000	0.000	0.964	1.848	2.864	2.614	3.647	0.263	1.243	0.000	1.000	1.624	4.458	5.127	0.307	0.138	0.737
S009	0.000	0.000	1.000	1.347	2.064	2.250	2.512	0.296	0.635	0.354	1.000	1.284	3.324	3.229	0.323	0.179	0.598
S008	2.000	1.000	0.667	3.125	0.986	1.307	1.290	0.024	0.000	0.000
S007	0.000	0.000	0.929	1.095	1.324	1.792	2.001	0.211	0.702	0.707	1.000	0.765	1.994	2.009	0.354	0.135	0.828
S006	0.000	0.000	1.000	1.950	2.418	2.383	3.761	0.290	0.960	0.354	1.000	0.930	3.353	3.407	0.324	0.131	0.556
S005	0.000	0.000	1.000	1.828	2.952	2.998	3.546	0.163	0.577	0.707	1.000	1.330	4.404	4.252	0.146	0.115	0.705
S004	0.000	1.000	0.893	5.980	2.109	2.576	3.286	0.187	0.713	0.354	1.000	1.467	3.221	3.567	0.378	0.156	0.703
S003	2.000	0.000	0.952	0.955	1.375	2.160	1.962	0.216	0.847	0.000	0.947	0.719	1.588	3.709	0.102	0.098	0.819
S002	0.000	0.000	0.917	1.690	2.410	2.997	2.706	0.254	0.571	0.354	1.000	0.798	2.273	1.960	0.159	0.146	0.624
S001	0.000	0.500	0.728	1.912	2.523	4.542	7.272	0.179	0.659	0.707

# Inspection: select(sub[sub$Participant == "S199", ], starts_with("AttentionCheck"))

sub[
  sub$Participant %in% outliers_questionnaires,
  names(sub)[!names(sub) %in% c(
    "Participant", "Nationality", "Age",
    "Ethnicity", "Sex", "Student", "Education",
    "Interval", "AttentionCheck_Session1",
    "AttentionCheck_Session2"
  )]
] <- NA

Final Sample

We collected data from 500 participants.

illusion1 <- illusion1[!illusion1$Participant %in% outliers_illusion1, ]
illusion2 <- illusion2[!illusion2$Participant %in% outliers_illusion1, ]
perceptual <- perceptual[!perceptual$Participant %in% outliers_illusion1, ]
sub <- sub[!sub$Participant %in% outliers_illusion1, ]

The final sample included 497 participants (Mean age = 30.0, SD = 10.1, range: [18, 73]; Sex: 49.9% females, 50.1% males, 0.0% other; Education: Prefer not to say, 0.20%; Other, 2.41%; High school, 34.21%; Bachelor, 45.07%; Master, 16.30%; Doctorate, 1.81%), from which 444 (89.34%) completed session 2.

Country of Origin

p_country <- select(sub, region = Nationality) |>
  group_by(region) |>
  summarize(n = n()) |>
  right_join(map_data("world"), by = "region") |>
  ggplot(aes(long, lat, group = group)) +
  geom_polygon(aes(fill = n)) +
  scale_fill_gradientn(colors = c("#FFEB3B", "red", "purple")) +
  labs(fill = "N") +
  theme_void() +
  labs(title = "Origin", subtitle = "Number of participants by country of origin")  +
  theme(
    plot.title = element_text(size = rel(1), face = "bold", hjust = 0)
  )
p_country

Age

p_age <- estimate_density(sub$Age) |>
  normalize(select = y) |> 
  mutate(y = y * 86) |>  # To match the binwidth
  ggplot(aes(x = x)) +
  geom_histogram(data=sub, aes(x = Age), fill = "#616161", bins=28) +
  geom_line(aes(y = y), color = "orange", linewidth=2) +
  geom_vline(xintercept = mean(sub$Age), color = "red", linewidth=1.5) +
  geom_label(data = data.frame(x = mean(sub$Age) * 1.15, y = 0.95 * 75), aes(y = y), color = "red", label = paste0("Mean = ", format_value(mean(sub$Age)))) +
  scale_x_continuous(expand = c(0, 0)) +
  scale_y_continuous(expand = c(0, 0)) +
  labs(title = "Age", y = "Number of Participants", color = NULL, subtitle = "Distribution of participants' age") +
  theme_modern(axis.title.space = 10) +
  theme(
    plot.title = element_text(size = rel(1), face = "bold", hjust = 0),
    plot.subtitle = element_text(vjust = 7),
    axis.text.y = element_text(size = rel(0.8)),
    axis.text.x = element_text(size = rel(0.8)),
    axis.title.x = element_blank()
  )
p_age

Ethnicity

plot_waffle <- function(sub, what = "Nationality", title = what, rows = 8, size = 3) {
  ggwaffle::waffle_iron(sub, what, rows = rows) |>
    ggplot(aes(x, y)) +
    geom_point(aes(color = group), shape = "square", size = size) +
    coord_equal() +
    ggtitle(title) +
    labs(fill = "", color = "", subtitle = "Number of participants per self-declared group (each square represents one participant)") +
    theme_void() +
    theme(
      plot.title = element_text(face = "bold", hjust = 0),
      legend.key.height = unit(1, "mm"),
      legend.key.width = unit(1, "mm")
    )
}

p_ethni <- plot_waffle(sub, "Ethnicity", rows = 20, size = 8) +
  scale_color_manual(values = c("Hispanic" = "#FF5722", "Caucasian" = "#2196F3", "African" = "#4CAF50", "Asian" = "#FFC107", "Other" = "#795548"))
p_ethni

Education

p_edu <- sub |>
  ggplot(aes(x = Education)) +
  geom_bar(aes(fill = Education)) +
  scale_y_continuous(expand = c(0, 0), breaks= scales::pretty_breaks()) +
  scale_fill_viridis_d(guide = "none") +
  labs(title = "Education", y = "Number of Participants", subtitle = "Number of participants per achieved education level") +
  theme_modern(axis.title.space = 15) +
  theme(
    plot.title = element_text(size = rel(1), face = "bold", hjust = 0),
    plot.subtitle = element_text(vjust = 7),
    axis.text.y = element_text(size = rel(0.8)),
    axis.text.x = element_text(size = rel(0.8)),
    axis.title.x = element_blank()
  )
p_edu

load_plot <- function(x) patchwork::wrap_elements(grid::rasterGrob(png::readPNG(x), interpolate = TRUE))

p <- (
  load_plot("figures/1_Preprocessing-p_demographics_country-1.png") |
    load_plot("figures/1_Preprocessing-p_demographics_age-1.png")) / (
  load_plot("figures/1_Preprocessing-p_demographics_ethnicity-1.png") |
    load_plot("figures/1_Preprocessing-p_demographics_education-1.png")
) + plot_annotation(title = "Demographic Information", subtitle = paste0("N = ", nrow(sub), " (49.9% ♀)"),
                    theme = theme(plot.title = element_text(size = rel(1.5), face = "bold", hjust = 0.5),
                                  plot.subtitle = element_text(size = rel(1.1), face = "italic", hjust = 0.5)))
p

ggsave("figures/figure_demographics.png", p, width = 12 * sqrt(2), height=12)

Save Preprocessed

write.csv(illusion1, "../data/preprocessed_illusion1.csv", row.names = FALSE)
write.csv(illusion2, "../data/preprocessed_illusion2.csv", row.names = FALSE)
write.csv(perceptual, "../data/preprocessed_perceptual.csv", row.names = FALSE)
write.csv(sub, "../data/preprocessed_questionnaires.csv", row.names = FALSE)